Statistics of
the Stability Bounds in the Phase Retrieval Problem

\nIn this talk w
e present a local-global Lipschitz analysis of the phase retrieval problem
. Additionally we present tentative estimates of the tail-bound for the di
stribution of the global Lipschitz constants. Specifically it is known tha
t if the frame {f1\,…\,fm} for Cn is phase retrievable then there are cons
tants a0 and b0 so that for every x\,y∈Cn: a0∣∣xx*-yy*∣∣12≤∑k=1m∣∣〈x\,fk〉∣
2-∣〈y\,fk〉∣2∣2≤b0∣∣xx*-yy*∣∣12. Assumef1\,…\,fm are independent realizatio
ns with entries from CN(0\,1). In this talk we establish estimates for the
probability P(a0>a).

Title: To Rep lace or Not to Replace in Finite Population Sampling

\nAbstract:

\nWe revisit the classical result in finite population sampling which s
tates that in *equally-likely *“simple” random sampling the sample
mean is more reliable when we do not replace after each draw. In this tal
k\, we review a classical result for the equally likely sampling case. The
n we investigate if and when the same is true for samples where it may no
longer be true that each member of the population has an equal chance of b
eing selected\, and when the population mean is estimated using the Horvit
z-Thompson inverse probability weighing to produce an unbiased estimator.
For a certain class of sampling schemes\, we are able to obtain convenien
t expressions for the variance of the sample mean and surprisingly\, we fi
nd that for some selection distributions a more reliable estimate of the p
opulation mean will happen by replacing after each draw. We show for sele
ction distributions lying in a certain polytope the classical result preva
ils.

\n

This is joint work with Fred Torcaso.

\n< /HTML> END:VEVENT BEGIN:VEVENT UID:ai1ec-7272@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION:Finite-Sample Bounds for Geometric Multiresolution Analysis\n DTSTART;TZID=America/New_York:20160914T150000 DTEND;TZID=America/New_York:20160914T160000 SEQUENCE:0 SUMMARY:Data Seminar: Nate Strawn (Georgetown University) @ Krieger 309 URL:https://engineering.jhu.edu/ams/events/data-seminar-nate-strawn-georget own-university-krieger-309/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nFinite-Sample Bounds for Geometric Multiresolution Analysis

\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-6996@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Stochastic Newton Methods for Machine Learning\nOptimization me thods play a crucial role in supervised learning where they are employed t o solve problems in very high dimensional parameter spaces. The optimizati on problems are inherently stochastic and often involve huge data sets. Th ere has recently been much interest in sub-sampled Newton methods for thes e types of applications. The methods use approximations to the gradient an d Hessian in a way that strikes a balance between computational effort and speed of convergence. We provide a review and analysis of sub-sampled New ton methods\, which include Newton-sketch and non-uniform subsampling tech niques\, and illustrate their effectiveness on some large-scale machine le arning applications.\n \nJorge Nocedal is the David and Karen Sachs Profes sor of Industrial Engineering and Management Sciences at Northwestern Univ ersity. He received his PhD in Mathematical Sciences from Rice University and was a postdoctoral fellow at the Courant Institute. His research is in nonlinear optimization with applications to machine learning. Over the ye ars\, his work has spanned algorithms\, analysis and software. He is a SIA M Fellow\, has been an invited speaker at the International Congress of Ma thematicians\, and was awarded the 2012 George B. Dantzig Prize.\n DTSTART;TZID=America/New_York:20160915T133000 DTEND;TZID=America/New_York:20160915T143000 SEQUENCE:0 SUMMARY:Goldman Lecture Series: Jorge Nocedal (Northwestern University)- Ma ryland 110 URL:https://engineering.jhu.edu/ams/events/goldman-lecture-series-jorge-noc edal-northwestern-university-location-tbd/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Stochastic Newton Methods fo
r Machine Learning**

Optimization methods play a crucial role in supervised learning where they are employed to solve problems in very high dimension al parameter spaces. The optimization problems are inherently stochastic a nd often involve huge data sets. There has recently been much interest in sub-sampled Newton methods for these types of applications. The methods us e approximations to the gradient and Hessian in a way that strikes a balan ce between computational effort and speed of convergence. We provide a rev iew and analysis of sub-sampled Newton methods\, which include Newton-sket ch and non-uniform subsampling techniques\, and illustrate their effective ness on some large-scale machine learning applications.

\n\n

Jorge Nocedal is the David and Karen Sachs Professor of Industrial Engineering and Managem ent Sciences at Northwestern University. He received his PhD in Mathematic al Sciences from Rice University and was a postdoctoral fellow at the Cour ant Institute. His research is in nonlinear optimization with applications to machine learning. Over the years\, his work has spanned algorithms\, a nalysis and software. He is a SIAM Fellow\, has been an invited speaker at the International Congress of Mathematicians\, and was awarded the 2012 G eorge B. Dantzig Prize.

\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7352@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:The JHU Actuarial Club will host an event on September 16th. T he speaker JHU alum\, Matt Sedlock\, is currently working at Mass Mutual. He will share his experience working in the actuarial industry and discus s the recruiting process from Mass Mutual DTSTART;TZID=America/New_York:20160916T180000 DTEND;TZID=America/New_York:20160916T200000 SEQUENCE:0 SUMMARY:What is an Actuary? @ Arellano Theatre\, Levering Hall URL:https://engineering.jhu.edu/ams/events/actuary-arellano-theatre-leverin g-hall/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

The JHU Actua rial Club will host an event on September 16th. The speaker JHU alum\, Ma tt Sedlock\, is currently working at Mass Mutual. He will share his exper ience working in the actuarial industry and discuss the recruiting process from Mass Mutual

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7276@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION: DTSTART;TZID=America/New_York:20160921T150000 DTEND;TZID=America/New_York:20160921T160000 SEQUENCE:0 SUMMARY:Data Seminar: Charles Meneveau (JHU) @ Krieger 309 URL:https://engineering.jhu.edu/ams/events/data-seminar-charles-meneveau-jh u-shaffer-100/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-6964@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:High-Dimensional Analysis of Stochastic Algorithms for Convex a nd Nonconvex Optimization: Limiting Dynamics and Phase Transitions\n \nAbs tract\nWe consider efficient iterative methods (e.g.\, stochastic gradient descent\, randomized Kaczmarz algorithms\, iterative coordinate descent) for solving large-scale optimization problems\, whether convex or nonconve x. A flurry of recent work has focused on establishing their theoretical p erformance guarantees. This intense interest is spurred on by the remarkab ly impressive empirical performance achieved by these low-complexity and m emory-efficient methods.\nIn this talk\, we will present a framework for a nalyzing the exact dynamics of these methods in the high-dimensional limit . For concreteness\, we consider two prototypical problems: regularized li near regression (e.g. LASSO) and sparse principal component analysis. For each case\, we show that the time-varying estimates given by the algorithm s will converge weakly to a deterministic “limiting process” in the high-d imensional (scaling and mean-field) limit. Moreover\, this limiting proces s can be characterized as the unique solution of a nonlinear PDE\, and it provides exact information regarding the asymptotic performance of the alg orithms. For example\, performance metrics such as the MSE\, the cosine si milarity and the misclassification rate in sparse support recovery can all be obtained by examining the deterministic limiting process. A steady-sta te analysis of the nonlinear PDE also reveals interesting phase transition phenomenons related to the performance of the algorithms. Although our an alysis is asymptotic in nature\, numerical simulations show that the theor etical predictions are accurate for moderate signal dimensions.\nWhat make s our analysis tractable is the notion of exchangeability\, a fundamental property of symmetry that is inherent in many of the optimization problems encountered in signal processing and machine learning.\n \nBio\nYue M. Lu was born in Shanghai. After finishing undergraduate studies at Shanghai J iao Tong University\, he attended the University of Illinois at Urbana-Cha mpaign\, where he received the M.Sc. degree in mathematics and the Ph.D. d egree in electrical engineering\, both in 2007. He was a Research Assistan t at the University of Illinois at Urbana-Champaign\, and has worked for M icrosoft Research Asia\, Beijing\, and Siemens Corporate Research\, Prince ton\, NJ. Following his work as a postdoctoral researcher at the Audiovisu al Communications Laboratory at Ecole Polytechnique Fédérale de Lausanne ( EPFL)\, Switzerland\, he joined Harvard University in 2010\, where he is c urrently an Associate Professor of Electrical Engineering at the John A. P aulson School of Engineering and Applied Sciences.\nHe received the Most I nnovative Paper Award (with Minh N. Do) of IEEE International Conference o n Image Processing (ICIP) in 2006\, the Best Student Paper Award of IEEE I CIP in 2007\, and the Best Student Presentation Award at the 31st SIAM SEA S Conference in 2007. Student papers supervised and coauthored by him won the Best Student Paper Award (with Ivan Dokmanic and Martin Vetterli) of I EEE International Conference on Acoustics\, Speech and Signal Processing i n 2011 and the Best Student Paper Award (with Ameya Agaskar and Chuang Wan g) of IEEE Global Conference on Signal and Information Processing (GlobalS IP) in 2014.\nHe has been an Associate Editor of the IEEE Transactions on Image Processing since 2014\, an Elected Member of the IEEE Image\, Video\ , and Multidimensional Signal Processing Technical Committee since 2015\, and an Elected Member of the IEEE Signal Processing Theory and Methods Tec hnical Committee since 2016. He received the ECE Illinois Young Alumni Ach ievement Award in 2015. DTSTART;TZID=America/New_York:20160922T133000 DTEND;TZID=America/New_York:20160922T143000 SEQUENCE:0 SUMMARY:Seminar: Yue Lu (Harvard) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-yue-lu-harvard-whitehead -304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**High-
Dimensional Analysis of Stochastic Algorithms for Convex and Nonconvex Opt
imization: Limiting Dynamics and Phase Transitions**

\n

**Abstract**

We consider efficient iterative met hods (e.g.\, stochastic gradient descent\, randomized Kaczmarz algorithms\ , iterative coordinate descent) for solving large-scale optimization probl ems\, whether convex or nonconvex. A flurry of recent work has focused on establishing their theoretical performance guarantees. This intense intere st is spurred on by the remarkably impressive empirical performance achiev ed by these low-complexity and memory-efficient methods.

\nIn this t alk\, we will present a framework for analyzing the exact dynamics of thes e methods in the high-dimensional limit. For concreteness\, we consider tw o prototypical problems: regularized linear regression (e.g. LASSO) and sp arse principal component analysis. For each case\, we show that the time-v arying estimates given by the algorithms will converge weakly to a determi nistic “limiting process” in the high-dimensional (scaling and mean-field) limit. Moreover\, this limiting process can be characterized as the uniqu e solution of a nonlinear PDE\, and it provides exact information regardin g the asymptotic performance of the algorithms. For example\, performance metrics such as the MSE\, the cosine similarity and the misclassification rate in sparse support recovery can all be obtained by examining the deter ministic limiting process. A steady-state analysis of the nonlinear PDE al so reveals interesting phase transition phenomenons related to the perform ance of the algorithms. Although our analysis is asymptotic in nature\, nu merical simulations show that the theoretical predictions are accurate for moderate signal dimensions.

\nWhat makes our analysis tractable is the notion of exchangeability\, a fundamental property of symmetry that is inherent in many of the optimization problems encountered in signal proce ssing and machine learning.

\n\n

**Bio**

Yue M. Lu was born in Shanghai. After finishing undergraduate studies at Shanghai Jiao Tong University\, he attended the University of Illinois at Urbana-Champaign\, where he received the M.Sc. degree in mathematics and t he Ph.D. degree in electrical engineering\, both in 2007. He was a Researc h Assistant at the University of Illinois at Urbana-Champaign\, and has wo rked for Microsoft Research Asia\, Beijing\, and Siemens Corporate Researc h\, Princeton\, NJ. Following his work as a postdoctoral researcher at the Audiovisual Communications Laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL)\, Switzerland\, he joined Harvard University in 2010\, whe re he is currently an Associate Professor of Electrical Engineering at the John A. Paulson School of Engineering and Applied Sciences.

\nHe re ceived the Most Innovative Paper Award (with Minh N. Do) of IEEE Internati onal Conference on Image Processing (ICIP) in 2006\, the Best Student Pape r Award of IEEE ICIP in 2007\, and the Best Student Presentation Award at the 31st SIAM SEAS Conference in 2007. Student papers supervised and coaut hored by him won the Best Student Paper Award (with Ivan Dokmanic and Mart in Vetterli) of IEEE International Conference on Acoustics\, Speech and Si gnal Processing in 2011 and the Best Student Paper Award (with Ameya Agask ar and Chuang Wang) of IEEE Global Conference on Signal and Information Pr ocessing (GlobalSIP) in 2014.

\nHe has been an Associate Editor of t he IEEE Transactions on Image Processing since 2014\, an Elected Member of the IEEE Image\, Video\, and Multidimensional Signal Processing Technical Committee since 2015\, and an Elected Member of the IEEE Signal Processin g Theory and Methods Technical Committee since 2016. He received the ECE I llinois Young Alumni Achievement Award in 2015.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7280@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION:From Molecular Dynamics to Large Scale Inference\n\nMolecular m odels and data analytics problems give rise to very large systems of stoch astic differential equations (SDEs) whose paths are designed to ergodicall y sample multimodal probability distributions. An important challenge for the numerical analyst (or the data scientist\, for that matter) is the des ign of numerical procedures to generate these paths. One of the interestin g ideas is to construct stochastic numerical methods with close attention to the error in the invariant measure. Another is to redesign the underlyi ng stochastic dynamics to reduce bias or locally transform variables to en hance sampling efficiency. I will illustrate these ideas with various exam ples\, including a geodesic integrator for constrained Langevin dynamics [ 1] and an ensemble sampling strategy for distributed inference [2]. DTSTART;TZID=America/New_York:20160923T150000 DTEND;TZID=America/New_York:20160923T160000 SEQUENCE:0 SUMMARY:Data Seminar: Ben Leimkuhler (University of Edinburgh) @ TBD URL:https://engineering.jhu.edu/ams/events/data-seminar-ben-leimkuhler-univ ersity-edinburgh-tbd/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nFrom Molecular Dynamics to Large Scale Inference

\n

\nMolecular models and data analytics problems give rise to v
ery large systems of stochastic differential equations (SDEs) whose paths
are designed to ergodically sample multimodal probability distributions. A
n important challenge for the numerical analyst (or the data scientist\, f
or that matter) is the design of numerical procedures to generate these pa
ths. One of the interesting ideas is to construct stochastic numerical met
hods with close attention to the error in the invariant measure. Another i
s to redesign the underlying stochastic dynamics to reduce bias or locally
transform variables to enhance sampling efficiency. I will illustrate the
se ideas with various examples\, including a geodesic integrator for const
rained Langevin dynamics [1] and an ensemble sampling strategy for distrib
uted inference [2].

Title: Edge-c oloring Multigraphs

\nAbstract: Graph (vertex) coloring is a central area of discrete math\; however\, it is NP-hard even to approximate the c hromatic number. Edge-coloring can be seen as a special case of vertex co loring. As such\, we may hope that computing (or approximating) the edge chromatic number is easier\; in fact\, it is. We will survey a number of theorems and conjectures on edge-coloring. These include many instances w hen the edge-chromatic number satisfies a trivial lower bound with equalit y\, such as when it equals the graph’s maximum degree. I will also mentio n some of my recent work in this area\, and introduce one of the main tool s\, Tashkinov trees\, which rely on a beautiful double induction.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7439@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION:Geometric Methods for the Approximation of High-dimensional Dyn amical Systems\n\nI will discuss a geometry-based statistical learning fra mework for performing model reduction and modeling of stochastic high-dime nsional dynamical systems. I will consider two complementary settings. In the first one\, I am given long trajectories of a system\, e.g. from molec ular dynamics\, and I discuss techniques for estimating\, in a robust fash ion\, an effective number of degrees of freedom of the system\, which may vary in the state space of then system\, and a local scale where the dynam ics is well-approximated by a reduced dynamics with a small number of degr ees of freedom. I will then use these ideas to produce an approximation to the generator of the system and obtain\, via eigenfunctions of an empiric al Fokker-Planck question\, reaction coordinates for the system that captu re the large time behavior of the dynamics. I will present various example s from molecular dynamics illustrating these ideas. In the second setting I assume I only have access to a (large number of expensive) simulators th at can return short simulations of high-dimensional stochastic system\, an d introduce a novel statistical learning framework for learning automatica lly a family of local approximations to the system\, that can be (automati cally) pieced together to form a fast global reduced model for the system\ , called ATLAS. ATLAS is guaranteed to be accurate (in the sense of produc ing stochastic paths whose distribution is close to that of paths generate d by the original system) not only at small time scales\, but also at larg e time scales\, under suitable assumptions on the dynamics. I discuss appl ications to homogenization of rough diffusions in low and high dimensions\ , as well as relatively simple systems with separations of time scales\, a nd deterministic chaotic systems in high-dimensions\, that are well-approx imated by stochastic differential equations.\nNo knowledge of molecular dy namics is required\, and the techniques above are quite universal. Ideas i n the first part of the talk are based on what is called Diffusion Geometr y\, and have been used widely in data analysis\; ideas in the second part are applicable to MCMC. The talk will be accessible to students with a wid e variety of backgrounds and interests. DTSTART;TZID=America/New_York:20161005T150000 DTEND;TZID=America/New_York:20161005T160000 SEQUENCE:0 SUMMARY:Data Seminar: Mauro Maggioni (JHU) @ Krieger 309 URL:https://engineering.jhu.edu/ams/events/data-seminar-mauro-maggioni-jhu- krieger-309/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nGeometric Methods for the Approximation of High-dimensional Dynamical
Systems

\n

\nI will discuss a geometry-base
d statistical learning framework for performing model reduction and modeli
ng of stochastic high-dimensional dynamical systems. I will consider two c
omplementary settings. In the first one\, I am given long trajectories of
a system\, e.g. from molecular dynamics\, and I discuss techniques for est
imating\, in a robust fashion\, an effective number of degrees of freedom
of the system\, which may vary in the state space of then system\, and a l
ocal scale where the dynamics is well-approximated by a reduced dynamics w
ith a small number of degrees of freedom. I will then use these ideas to p
roduce an approximation to the generator of the system and obtain\, via ei
genfunctions of an empirical Fokker-Planck question\, reaction coordinates
for the system that capture the large time behavior of the dynamics. I wi
ll present various examples from molecular dynamics illustrating these ide
as. In the second setting I assume I only have access to a (large number o
f expensive) simulators that can return short simulations of high-dimensio
nal stochastic system\, and introduce a novel statistical learning framewo
rk for learning automatically a family of local approximations to the syst
em\, that can be (automatically) pieced together to form a fast global red
uced model for the system\, called ATLAS. ATLAS is guaranteed to be accura
te (in the sense of producing stochastic paths whose distribution is close
to that of paths generated by the original system) not only at small time
scales\, but also at large time scales\, under suitable assumptions on th
e dynamics. I discuss applications to homogenization of rough diffusions i
n low and high dimensions\, as well as relatively simple systems with sepa
rations of time scales\, and deterministic chaotic systems in high-dimensi
ons\, that are well-approximated by stochastic differential equations.

*No knowledge of molecular dynamics is required\, and the technique
s above are quite universal. Ideas in the first part of the talk are based
on what is called Diffusion Geometry\, and have been used widely in data
analysis\; ideas in the second part are applicable to MCMC. The talk will
be accessible to students with a wide variety of backgrounds and interests
.*

Title:

\n< p>Stochastic Search Methods for Simulation Optimization\n\n

Abstract:

\nA variety of systems arising in finance\, engineering de sign\, and manufacturing require the use of optimization techniques to imp rove their performance. Due to the complexity and stochastic dynamics of s uch systems\, their performance evaluation frequently requires computer si mulation\, which however often lacks structure needed by classical optimiz ation methods. We developed a gradient-based stochastic search approach\, based on the idea of converting the original (structure-lacking) problem t o a differentiable optimization problem on the parameter space of a sampli ng distribution that guides the search. A two-timescale updating scheme is further studied and incorporated to improve the algorithm efficiency. Con vergence properties of our approach are established through techniques fro m stochastic approximation\, and the performance of our algorithms is illu strated in comparison with some state-of-the-art simulation optimization m ethods. This is a joint work with Jiaqiao Hu (Stony Brook University) and Shalabh Bhartnagar (Indian Institute of Science).

\n\n

Biogra phy:

\nEnlu Zhou is currently an associate professor in the H. Milto n School of Industrial & Systems Engineering at Georgia Institute of Techn ology. Prior to joining Georgia Tech in 2013\, she was an assistant profe ssor in the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign from 2009-2013. She received the B .S. degree with highest honors in electrical engineering from Zhejiang Uni versity\, China\, in 2004\, and the Ph.D. degree in electrical engineering from the University of Maryland\, College Park\, in 2009. Her research in terests include stochastic control\, simulation optimization\, and Monte C arlo statistical methods. She is a recipient of the “Best Theoretical Pape r” award at the Winter Simulation Conference in 2009\, AFOSR Young Investi gator award in 2012\, and NSF CAREER award in 2015.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7463@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20161010T190000 DTEND;TZID=America/New_York:20161010T213000 SEQUENCE:0 SUMMARY:HUSAM Event: Global Hedge Fund Marshall Wace Recruiting Event URL:https://engineering.jhu.edu/ams/events/husam-event-global-hedge-fund-ma rshall-wace-recruiting-event/ X-COST-TYPE:free X-WP-IMAGES-URL:thumbnail\;http://engineering.jhu.edu/ams/wp-content/upload s/sites/44/2016/10/Husam-300x232.jpg\;730\;565\,medium\;http://engineering .jhu.edu/ams/wp-content/uploads/sites/44/2016/10/Husam-300x232.jpg\;730\;5 65\,large\;http://engineering.jhu.edu/ams/wp-content/uploads/sites/44/2016 /10/Husam-300x232.jpg\;730\;565\,full\;http://engineering.jhu.edu/ams/wp-c ontent/uploads/sites/44/2016/10/Husam-300x232.jpg\;730\;565 X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nModeling the dynamics of interacting particles by means of stochastic
networks

\n

\nMaterial science have been ra
pidly developing in recent years. A variety of particles interacting accor
ding to different kinds of pair potentials has been produced in experiment
al works. Looking into the future\, one can imagine controlled self-assemb
ly of particles into clusters of desired structures leading to the creatio
n of new types of materials. Analytical studies of the self-assembly invol
ve coping with difficulties associated with the huge numbers configuration
s\, high dimensionality\, complex geometry\, and unacceptably large CPU ti
mes. A feasible approach to the study of self-assembly consists of mapping
the collections of clusters onto stochastic networks (continuous-time Mar
kov chains) and analyzing their dynamics. Vertices of the networks represe
nt local minima of the potential energy of the clusters\, while arcs conne
ct only those pairs of vertices that correspond to local minima between wh
ich direct transitions are physically possible. Transition rates along the
arcs are the transition rates between the corresponding pairs of local mi
nima. Such networks are mathematically tractable and\, at the same time\,
preserve important features of the underlying dynamics. Nevertheless\, the
ir huge size and complexity render their analysis challenging and invoke t
he development of new mathematical techniques. I will discuss some approac
hes to construction and analysis of such networks.

Title:

\n< p>Leveraged Funds: Robust Replication and Performance Evaluation\nA bstract:

\nLeveraged and inverse ETFs seek a daily return equal to a multiple of an index’ return\, an objective that requires continuous port folio rebalancing. The resulting trading costs create a tradeoff between t racking error\, which controls the short-term correlation with the index\, and excess return (or tracking difference) – the long-term deviation from the leveraged index’ performance. With proportional trading costs\, the o ptimal replication policy is robust to the index’ dynamics. A summary of a fund’s performance is the implied spread\, equal to the product of tracki ng error and excess return\, rescaled for leverage and average volatility. The implied spread is insensitive to the benchmark’s risk premium and off ers a tool to compare the performance of funds tracking the same index wit h different factors and tracking errors.

\n\n

http://ssrn.com /abstract=2839852

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7300@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION: DTSTART;TZID=America/New_York:20161026T150000 DTEND;TZID=America/New_York:20161026T160000 SEQUENCE:0 SUMMARY:Data Seminar: Robert Pego (Carnegie Mellon University) @ Krieger 30 9 URL:https://engineering.jhu.edu/ams/events/data-seminar-robert-pego-carnegi e-mellon-university-krieger-309/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-6963@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Adaptive Contrast Weighted Learning and Tree-based Reinforcemen t Learning for Multi-Stage Multi-Treatment Decision-Making\nDynamic treatm ent regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. We develop robus t and flexible semiparametric and machine learning methods for estimating optimal DTRs. In this talk\, we present a dynamic statistical learning met hod\, adaptive contrast weighted learning (ACWL)\, which combines doubly r obust semiparametric regression estimators with flexible machine learning methods. ACWL can handle multiple treatments at each stage and does not re quire prespecifying candidate DTRs. At each stage\, we develop robust semi parametric regression-based contrasts with the adaptation of treatment eff ect ordering for each patient\, and the adaptive contrasts simplify the pr oblem of optimization with multiple treatment comparisons to a weighted cl assification problem that can be solved with existing machine learning tec hniques. We further develop a tree-based reinforcement learning (T-RL) met hod to directly estimate optimal DTRs in a multi-stage multi-treatment set ting. At each stage\, T-RL builds an unsupervised decision tree that maint ains the nature of batch-mode reinforcement learning. Unlike ACWL\, T-RL h andles the optimization problem with multiple treatment comparisons direct ly through the purity measure constructed with augmented inverse probabili ty weighted estimators. By combining robust semiparametric regression with flexible tree-based learning\, T-RL is robust\, efficient and easy to int erpret for the identification of optimal DTRs. However\, ACWL seems more r obust to tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. We illustrate the performances of both methods in simulati ons and case studies. DTSTART;TZID=America/New_York:20161027T133000 DTEND;TZID=America/New_York:20161027T143000 SEQUENCE:0 SUMMARY:Seminar: Lu Wang (University of Michigan) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-lu-wang-university-of-mi chigan-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Adapt
ive Contrast Weighted Learning and Tree-based Reinforcement Learning for M
ulti-Stage Multi-Treatment Decision-Making**

Dynamic treatm ent regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. We develop robus t and flexible semiparametric and machine learning methods for estimating optimal DTRs. In this talk\, we present a dynamic statistical learning met hod\, adaptive contrast weighted learning (ACWL)\, which combines doubly r obust semiparametric regression estimators with flexible machine learning methods. ACWL can handle multiple treatments at each stage and does not re quire prespecifying candidate DTRs. At each stage\, we develop robust semi parametric regression-based contrasts with the adaptation of treatment eff ect ordering for each patient\, and the adaptive contrasts simplify the pr oblem of optimization with multiple treatment comparisons to a weighted cl assification problem that can be solved with existing machine learning tec hniques. We further develop a tree-based reinforcement learning (T-RL) met hod to directly estimate optimal DTRs in a multi-stage multi-treatment set ting. At each stage\, T-RL builds an unsupervised decision tree that maint ains the nature of batch-mode reinforcement learning. Unlike ACWL\, T-RL h andles the optimization problem with multiple treatment comparisons direct ly through the purity measure constructed with augmented inverse probabili ty weighted estimators. By combining robust semiparametric regression with flexible tree-based learning\, T-RL is robust\, efficient and easy to int erpret for the identification of optimal DTRs. However\, ACWL seems more r obust to tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. We illustrate the performances of both methods in simulati ons and case studies.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7304@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION:Variational problems on graphs and their continuum limits\n\nWe will discuss variational problems arising in machine learning and their l imits as the number of data points goes to infinity. Consider point clouds obtained as random samples of an underlying “ground-truth” measure. Graph representing the point cloud is obtained by assigning weights to edges ba sed on the distance between the points. Many machine learning tasks\, such as clustering and classification\, can be posed as minimizing functionals on such graphs. We consider functionals involving graph cuts and graph la placians and their limits as the number of data points goes to infinity. I n particular we establish under what conditions the minimizers of discrete problems have a well defined continuum limit\, and characterize the limit . The talk is primarily based on joint work with Nicolas Garcia Trillos\, as well as on works with Xavier Bresson\, Moritz Gerlach\, Matthias Hein\, Thomas Laurent\, James von Brecht and Matt Thorpe. DTSTART;TZID=America/New_York:20161102T150000 DTEND;TZID=America/New_York:20161102T160000 SEQUENCE:0 SUMMARY:Data Seminar: Dejan Slepcev (Carnegie Mellon University) @ Krieger 309 URL:https://engineering.jhu.edu/ams/events/data-seminar-dejan-slepcev-carne gie-mellon-university-krieger-309/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nVariational problems on graphs and their continuum limits

\n

\nWe will discuss variational problems arising in ma
chine learning and their limits as the number of data points goes to infin
ity. Consider point clouds obtained as random samples of an underlying “gr
ound-truth” measure. Graph representing the point cloud is obtained by ass
igning weights to edges based on the distance between the points. Many mac
hine learning tasks\, such as clustering and classification\, can be posed
as minimizing functionals on such graphs. We consider functionals involvi
ng graph cuts and graph laplacians and their limits as the number of data
points goes to infinity. In particular we establish under what conditions
the minimizers of discrete problems have a well defined continuum limit\,
and characterize the limit. The talk is primarily based on joint work with
Nicolas Garcia Trillos\, as well as on works with Xavier Bresson\, Moritz
Gerlach\, Matthias Hein\, Thomas Laurent\, James von Brecht and Matt Thor
pe.

An Introducti on to Distance Preserving Projections of Smooth Manifolds

\n\n< p>Manifold-based image models are assumed in many engineering applications involving imaging and image classification. In the setting of image clas sification\, in particular\, proposed designs for small and cheap cameras motivate compressive imaging applications involving manifolds. Interestin g mathematics results when one considers that the problem one needs to sol ve in this setting ultimately involves questions concerning how well one c an embed a low-dimensional smooth sub-manifold of high-dimensional Euclide an space into a much lower dimensional space without knowing any of its de tailed structure. We will motivate this problem and discuss how one might accomplish this seemingly difficult task using random projections. Littl e if any prerequisites will be assumed beyond linear algebra and some prob ability.\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7308@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION:Scalable Information Inequalities for Uncertainty Quantificatio n in high dimensional probabilistic models\n\nIn this this talk we discuss new scalable information bounds for quantities of interest of complex sto chastic models. The scalability of inequalities allows us to (a) obtain un certainty quantification bounds for quantities of interest in high-dimensi onal systems and/or for long time stochastic dynamics\; (b) assess the imp act of large model perturbations such as in nonlinear response regimes in statistical mechanics\; (c) address model-form uncertainty\, i.e. compare different extended probabilistic models and corresponding quantities of in terest. We demonstrate these tools in fast sensitivity screening of chemic al reaction networks with a very large number of parameters\, and towards obtaining robust and tight uncertainty quantification bounds for phase dia grams in statistical mechanics models. DTSTART;TZID=America/New_York:20161109T150000 DTEND;TZID=America/New_York:20161109T160000 SEQUENCE:0 SUMMARY:Data Seminar: Markos Katsoulakis (University of Massachusetts Amher st) @ Krieger 309 URL:https://engineering.jhu.edu/ams/events/data-seminar-markos-katsoulakis- university-massachusetts-amherst-krieger-309/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Scalable Information Inequalities for Uncertainty Quantification in hi
gh dimensional probabilistic models

\n

\nIn
this this talk we discuss new scalable information bounds for quantities
of interest of complex stochastic models. The scalability of inequalities
allows us to (a) obtain uncertainty quantification bounds for quantities o
f interest in high-dimensional systems and/or for long time stochastic dyn
amics\; (b) assess the impact of large model perturbations such as in nonl
inear response regimes in statistical mechanics\; (c) address model-form u
ncertainty\, i.e. compare different extended probabilistic models and corr
esponding quantities of interest. We demonstrate these tools in fast sensi
tivity screening of chemical reaction networks with a very large number of
parameters\, and towards obtaining robust and tight uncertainty quantific
ation bounds for phase diagrams in statistical mechanics models.

**Slipp
ing Through the Cracks: Detecting Manipulation in Regional Commodity Marke
ts**

\n

Reid B. Stevens[1] and Jeffery Y. Zhang[2]\n

\n

Between 2010 and 2014\, the regional price of aluminum in
the United States (Midwest premium) increased 400 percent. We argue that
the Midwest premium was likely manipulated during this period through the
exercise of market power in the aluminum storage market. We first use a di
fference-in-differences model to show that there was a statistically signi
ficant increase of $0.07 per pound in the regional price of aluminum relat
ive to the regional price of a production complement\, copper. We then us
e several instrumental variables to show that this increase was driven by
a single financial company’s accumulation of an unprecedented level of alu
minum inventories in Detroit. Since this scheme targeted the regional pri
ce of aluminum\, regulators who monitored only spot and futures prices wou
ld not have noticed anything peculiar. We therefore present an algorithm f
or real-time detection of similar manipulation schemes in regional commodi
ty markets. The algorithm confirms the existence of a structural break in
the U.S. aluminum market in late 2011. Using the algorithm\, regulators c
ould have detected the scheme as early as December 2012\, more than six mo
nths before it was publicized by an article in *The New York Times*
. We also apply the algorithm to another suspected case of regional price
manipulation in the European aluminum market and find a similar break in 2
011\, suggesting the scheme may have been implemented beyond the United St
ates.

[1] Department of Agric ulture Economics\, Texas A&M University\, stevens@tamu.edu

\n[2] Department of Economics\, Yale Universit y and Harvard Law School\, jeffery.zhang@yale.edu

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7312@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION: DTSTART;TZID=America/New_York:20161130T150000 DTEND;TZID=America/New_York:20161130T160000 SEQUENCE:0 SUMMARY:Data Seminar: Youssef Marzouk (Massachusetts Institute of Technolog y) @ Krieger 309 URL:https://engineering.jhu.edu/ams/events/data-seminar-youssef-marzouk-mas sachusetts-institute-technology-shaffer-100/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-8216@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Spectral Clustering for Dynamic Stochastic Block Model\n \nAbstract: One of the most common and crucial aspects of many network da ta sets is the dependence of network link structure on time. In this work\ , we extend the existing (static) nonparametric latent variable model in t he context of time-varying networks\, and thereby propose a class of dyna mic network models. For some special cases of these models (namely the dyn amic stochastic block model and dynamic degree corrected block model)\, wh ich assume that there is a common clustering structure for all networks\, we consider the problem of identifying the common clustering structure. We propose two extensions of the (standard) spectral clustering method for t he dynamic network models\, and give theoretical guarantee that the spectr al clustering methods produce consistent community detection in case of bo th dynamic stochastic block model and dynamic degree-corrected block model . The methods are shown to work under sufficiently mild conditions on the number of time snapshots to detect both associative and dissociative comm unity structure\, even if all the individual networks are very sparse and most of the individual networks are below community detectability thresho ld. We reinforce the validity of the theoretical results via simulations t oo.\n(Joint work with Shirshendu Chatterjee\, CUNY) DTSTART;TZID=America/New_York:20161201T133000 DTEND;TZID=America/New_York:20161201T143000 SEQUENCE:0 SUMMARY:Seminar: Sharmodeep Bhattacharyya (Oregon State University) @ White head 304 URL:https://engineering.jhu.edu/ams/events/seminar-sharmodeep-bhattacharyya -oregon-state-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Spectr al Clustering for Dynamic Stochastic Block Model

\n\n

Abstrac t: One of the most common and crucial aspects of many network data sets is the dependence of network link structure on time. In this work\, we exten d the existing (static) nonparametric latent variable model in the context of time-varying networks\, and thereby propose a class of dynamic networ k models. For some special cases of these models (namely the dynamic stoch astic block model and dynamic degree corrected block model)\, which assume that there is a common clustering structure for all networks\, we conside r the problem of identifying the common clustering structure. We propose t wo extensions of the (standard) spectral clustering method for the dynamic network models\, and give theoretical guarantee that the spectral cluster ing methods produce consistent community detection in case of both dynamic stochastic block model and dynamic degree-corrected block model. The met hods are shown to work under sufficiently mild conditions on the number of time snapshots to detect both associative and dissociative community stru cture\, even if all the individual networks are very sparse and most of t he individual networks are below community detectability threshold. We rei nforce the validity of the theoretical results via simulations too.

\n< p>(Joint work with Shirshendu Chatterjee\, CUNY)\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7316@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION: DTSTART;TZID=America/New_York:20161207T150000 DTEND;TZID=America/New_York:20161207T160000 SEQUENCE:0 SUMMARY:Data Seminar: Ben Adcock (Simons Fraser University) @ Krieger 309 URL:https://engineering.jhu.edu/ams/events/data-seminar-ben-adcock-simons-f raser-university-shaffer-100/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-8240@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Online and Random-order Load Balancing Simultaneously\nA bstract: We consider the problem of online load balancing under lp-norms: sequential jobs need to be assigned to one of the machines and the goal is to minimize the lp-norm of the machine loads. This generalizes the classi cal problem of scheduling for makespan minimization (case l_infty) and has been thoroughly studied. We provide algorithms with simultaneously optima l* guarantees for the worst-case model as well as for the random-order (i. e. secretary) model\, where an arbitrary set of jobs comes in random order .\nOne of the main components is a new algorithm with improved regret for Online Linear Optimization (OLO) over the non-negative vectors in the lq b all. Interestingly\, this OLO algorithm is also used to prove a purely pro babilistic inequality that controls the correlations arising in the random -order model\, a common source of difficulty for the analysis. A property that drives both our load balancing algorithms and our OLO algorithm is a smoothing of the the lp-norm that may be of independent interest. DTSTART;TZID=America/New_York:20161208T133000 DTEND;TZID=America/New_York:20161208T143000 SEQUENCE:0 SUMMARY:Seminar: Marco Molinaro (Pontifical Catholic University of Rio de J aneiro) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-marco-molinaro-universit y-california-davis-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Online and Random-order Load Balancing Simultaneously

\nAbstract: We consi der the problem of online load balancing under lp-norms: sequential jobs n eed to be assigned to one of the machines and the goal is to minimize the lp-norm of the machine loads. This generalizes the classical problem of sc heduling for makespan minimization (case l_infty) and has been thoroughly studied. We provide algorithms with simultaneously optimal* guarantees for the worst-case model as well as for the random-order (i.e. secretary) mod el\, where an arbitrary set of jobs comes in random order.

\nOne of the main components is a new algorithm with improved regret for Online Lin ear Optimization (OLO) over the non-negative vectors in the lq ball. Inter estingly\, this OLO algorithm is also used to prove a purely probabilistic inequality that controls the correlations arising in the random-order mod el\, a common source of difficulty for the analysis. A property that drive s both our load balancing algorithms and our OLO algorithm is a smoothing of the the lp-norm that may be of independent interest.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-8220@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Spatial-temporal modeling of the association between air pollut ion exposures and birth outcomes: identifying critical exposure windows\nE xposure to high levels of air pollution during pregnancy has been linked t o increased probability of adverse birth outcomes. We consider statistical models for evaluating associations between pollutants and birth outcomes\ , taking into account multipollutant exposures\, susceptible windows in pr egnancy\, and variability in exposure over space and time. We consider geo coded vital records data from Texas as well as data from the National Birt h Defects Prevention Study. DTSTART;TZID=America/New_York:20170202T133000 DTEND;TZID=America/New_York:20170202T143000 SEQUENCE:0 SUMMARY:Wierman Lecture Series: Amy Herring (University of North Carolina C hapel Hill) @ Hodson 110 URL:https://engineering.jhu.edu/ams/events/wierman-lecture-series-amy-herri ng-university-north-carolina-chapel-hill-tba/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Spati
al-temporal modeling of the association between air pollution exposures an
d birth outcomes: identifying critical exposure windows**

E xposure to high levels of air pollution during pregnancy has been linked t o increased probability of adverse birth outcomes. We consider statistical models for evaluating associations between pollutants and birth outcomes\ , taking into account multipollutant exposures\, susceptible windows in pr egnancy\, and variability in exposure over space and time. We consider geo coded vital records data from Texas as well as data from the National Birt h Defects Prevention Study.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9274@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION: DTSTART;TZID=America/New_York:20170208T150000 DTEND;TZID=America/New_York:20170208T160000 SEQUENCE:0 SUMMARY:Data Seminar: Kasso Okoudjou (University of Maryland\, College Park ) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/data-seminar-kasso-okoudjou-univ ersity-maryland-college-park-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7404@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Geometry\, Shapes and PDEs\n \nAbstract:\nThe interest i n right invariant metrics on the diffeomorphism group is fueled by its rel ations to hydrodynamics. Arnold noted in 1966 that Euler’s equations\, whi ch govern the motion of ideal\, incompressible fluids\, can be interpreted as geodesic equations on the group of volume preserving diffeomorphisms w ith respect to a suitable Riemannian metric. Since then other PDEs arising in physics have been interpreted as geodesic equations on manifold of map pings. Examples include Burgers’ equation\, the KdV and Camassa-Holm equat ions or the Hunter-Saxton equation.\nAnother important motivation for the study of Riemannian metrics on manifold of mappings can be found in its a ppearance in the field of shape analysis and in particular in the eminent role of the diffeomorphism group in computational anatomy: the space of me dical images is acted upon by the diffeomorphism group and differences bet ween images are encoded by diffeomorphisms in the spirit of Grenander’s pa ttern theory. The study of anatomical shapes can be thus reduced to the st udy of the diffeomorphism group.\nUsing these observations as a starting p oint\, I will consider Riemannian metrics on spaces of mappings. I will di scuss the local and global well-posedness of the corresponding geodesic eq uation\, study the induced geodesic distance and present selected numerica l examples of minimizing geodesics. DTSTART;TZID=America/New_York:20170209T133000 DTEND;TZID=America/New_York:20170209T143000 SEQUENCE:0 SUMMARY:Seminar: Martin Bauer (Florida State University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-martin-bauer-florida-sta te-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Title: Geomet ry\, Shapes and PDEs

\n\n

Abstract:

\nThe interest in r ight invariant metrics on the diffeomorphism group is fueled by its relati ons to hydrodynamics. Arnold noted in 1966 that Euler’s equations\, which govern the motion of ideal\, incompressible fluids\, can be interpreted as geodesic equations on the group of volume preserving diffeomorphisms with respect to a suitable Riemannian metric. Since then other PDEs arising in physics have been interpreted as geodesic equations on manifold of mappin gs. Examples include Burgers’ equation\, the KdV and Camassa-Holm equation s or the Hunter-Saxton equation.

\nAnother important motivation for the study of Riemannian metrics on manifold of mappings can be found in i ts appearance in the field of shape analysis and in particular in the emin ent role of the diffeomorphism group in computational anatomy: the space o f medical images is acted upon by the diffeomorphism group and differences between images are encoded by diffeomorphisms in the spirit of Grenander’ s pattern theory. The study of anatomical shapes can be thus reduced to th e study of the diffeomorphism group.

\nUsing these observations as a starting point\, I will consider Riemannian metrics on spaces of mappings . I will discuss the local and global well-posedness of the corresponding geodesic equation\, study the induced geodesic distance and present select ed numerical examples of minimizing geodesics.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9278@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20170215T150000 DTEND;TZID=America/New_York:20170215T160000 SEQUENCE:0 SUMMARY:Data Seminar: Jerome Darbon (Brown University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/data-seminar-jerome-darbon-brown -university-whitehead-304/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-8440@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:TITLE:\nA local limit theorem for QuickSort key comparisons via multi-round smoothing\nABSTRACT:\nIt is a well-known result\, due indepen dently to Régnier (1989) and Rösler (1991)\, that the number of key compar isons required by the randomized sorting algorithm QuickSort to sort a lis t of n distinct items (keys) satisfies a global distributional limit the orem. We resolve an open problem of Fill and Janson from 2002 by using a multi-round smoothing technique to establish the corresponding local limit theorem. (in plain text\; note that only the “n” in “sort a list of n d istinct items” would be set in math mode in LaTeX)\nThis is joint work wit h Béla Bollobás and Oliver Riordan. DTSTART;TZID=America/New_York:20170216T133000 DTEND;TZID=America/New_York:20170216T143000 SEQUENCE:0 SUMMARY:Seminar: Jim Fill (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-jim-fill-jhu-whitehead-3 04/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**TITLE
:**

A local limit theorem for QuickSort key comparisons via multi-round smoothing

\n**ABSTRACT:**

It is a well-known result\, due independently to Régnier (1989) and Rösler (1991)\ , that the number of key comparisons required by the randomized sorting al gorithm QuickSort to sort a list of n distinct items (keys) satisfies a global distributional limit theorem. We resolve an open problem of Fill a nd Janson from 2002 by using a multi-round smoothing technique to establis h the corresponding local limit theorem. (in plain text\; note that only t he “n” in “sort a list of n distinct items” would be set in math mode in LaTeX)

\nThis is joint work with Béla Bollobás and Oliver Riordan.< /p>\n END:VEVENT BEGIN:VEVENT UID:ai1ec-8420@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20170223T133000 DTEND;TZID=America/New_York:20170223T143000 SEQUENCE:0 SUMMARY:Seminar: Laurent Younes (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-laurent-younes-jhu-white head-304/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-9442@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Link to the slides from Tom Loredo’s seminar- JHU17-HierBayesCo smicPopns DTSTART;TZID=America/New_York:20170302T133000 DTEND;TZID=America/New_York:20170302T143000 SEQUENCE:0 SUMMARY:Seminar: Tom Loredo (Cornell University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-tom-loredo-cornell-unive rsity-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Link to the s lides from Tom Loredo’s seminar- JHU17- HierBayesCosmicPopns

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9470@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20170307T130000 DTEND;TZID=America/New_York:20170307T140000 LOCATION:Hodson 3rd Floor Lobby SEQUENCE:0 SUMMARY:HUSAM Presents Coffee Chat URL:https://engineering.jhu.edu/ams/events/husam-presents-coffee-chat/ X-COST-TYPE:free X-WP-IMAGES-URL:thumbnail\;https://engineering.jhu.edu/ams/wp-content/uploa ds/sites/44/2017/03/Coffee_Chat-002-300x139.jpg\;300\;139\,medium\;https:/ /engineering.jhu.edu/ams/wp-content/uploads/sites/44/2017/03/Coffee_Chat-0 02-300x139.jpg\;300\;139\,large\;https://engineering.jhu.edu/ams/wp-conten t/uploads/sites/44/2017/03/Coffee_Chat-002-300x139.jpg\;300\;139\,full\;ht tps://engineering.jhu.edu/ams/wp-content/uploads/sites/44/2017/03/Coffee_C hat-002-300x139.jpg\;300\;139 X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Mean Field Games: theory and applications

\n\n

Abstract: We review the Mean Field Game paradigm introduced independently by Caines-Huang-Mal hame and Lasry-Lyons ten years ago\, and we illustrate their relevance to applications with a few practical of examples (bird flocking\, room exit\, systemic risk\, cyber-security\, …. ). We then review the probabilistic a pproach based on Forward-Backward Stochastic Differential Equations\, and we derive the Master Equation from a version of the chain rule (Ito’s for mula) for functions over flows of probability measures. Finally\, motivate d by the literature on economic models of bank runs\, we introduce mean fi eld games of timing and discuss new results\, and some of the many remaini ng challenges.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9266@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Mean Field Games with Major and Minor Players: Theory an d Numerics.\n \nAbstract: We present a (possibly) new formulation of the m ean field game problem in the presence of major and minor players\, and gi ve new existence results for linear quadratic models and models with finit e state spaces. We shall also provide numerical results illustrating the t heory and raising new challenging open problems. DTSTART;TZID=America/New_York:20170310T133000 DTEND;TZID=America/New_York:20170310T143000 SEQUENCE:0 SUMMARY:Duncan Lecture Series: Rene Carmona (Princeton) @ Krieger 205 URL:https://engineering.jhu.edu/ams/events/duncan-lecture-series-rene-carmo na-princeton-tba-2/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Mean F ield Games with Major and Minor Players: Theory and Numerics.

\n\n

Abstract: We present a (possibly) new formulation of the mean field game problem in the presence of major and minor players\, and give new exi stence results for linear quadratic models and models with finite state sp aces. We shall also provide numerical results illustrating the theory and raising new challenging open problems.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9483@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:HUSAM is hosting a professional event with Deloitte. The event will be an overview of consulting at Deloitte. A panel of Deloitte pract itioners will present on Deloitte’s BTA consulting track\, health analytic s\, and answer questions. DTSTART;TZID=America/New_York:20170313T183000 LOCATION:Arellano Theater in Levering Hall SEQUENCE:0 SUMMARY:HUSAM Deloitte Event URL:https://engineering.jhu.edu/ams/events/husam-deloitte-event/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nHUSAM is host ing a professional event with Deloitte. The event will be an overview of consulting at Deloitte. A panel of Deloitte practitioners will present on Deloitte’s BTA consulting track\, health analytics\, and answer questions .

\n X-INSTANT-EVENT:1 END:VEVENT BEGIN:VEVENT UID:ai1ec-9258@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Nuke the Clouds: Using nuclear norm optimization to remo ve clouds from satellite images\nAbstract: We discuss how to use the nucle ar norm and matrix factorization techniques to remove clouds from satellit e images. The talk will focus on discussing the key properties and variat ional inequalities that is commonly used in minimizing convex functions wi th a nuclear norm term. We will also contrast the convex formulations wit h the corresponding rank constrained problems that are highly non-convex\, but which are sometimes simpler to solve regardless. Finally we will sho w a lot of examples/demos of how this is working in practice. DTSTART;TZID=America/New_York:20170316T133000 DTEND;TZID=America/New_York:20170316T143000 SEQUENCE:0 SUMMARY:Seminar: Peder Olsen (IBM) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-peder-olsen-ibm-whitehea d-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Nuke t he Clouds: Using nuclear norm optimization to remove clouds from satellite images

\nAbstract: We discuss how to use the nuclear norm and matri x factorization techniques to remove clouds from satellite images. The ta lk will focus on discussing the key properties and variational inequalitie s that is commonly used in minimizing convex functions with a nuclear norm term. We will also contrast the convex formulations with the correspondi ng rank constrained problems that are highly non-convex\, but which are so metimes simpler to solve regardless. Finally we will show a lot of exampl es/demos of how this is working in practice.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9290@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION: DTSTART;TZID=America/New_York:20170329T150000 DTEND;TZID=America/New_York:20170329T160000 SEQUENCE:0 SUMMARY:Data Seminar: Jason Eisner (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/data-seminar-jason-eisner-jhu-wh itehead-304/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-8224@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Heuristics for Network Revenue Management\nWe consider a networ k revenue management problem with customer choice and exogenous prices. Su ch problems are central in several applications including airline ticket p ricing. Given the infeasibility of explicitly finding optimal policies\, w e study the performance of a class of heuristic policies. These heuristics periodically re-solve the deterministic linear program (DLP) that results when all future random variables are replaced by their average values and implement the solutions in a probabilistic manner. We provide an upper bo und for the expected revenue loss under such policies when compared to the optimal policy. Using this bound\, we construct a schedule of re-solving times such that the resulting expected revenue loss is bounded by a consta nt that is independent of the size of the problem.\nJoint work with Stefan us Jasin at University of Michigan.\n DTSTART;TZID=America/New_York:20170330T133000 DTEND;TZID=America/New_York:20170330T143000 SEQUENCE:0 SUMMARY:Seminar: Sunil Kumar (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-sunil-kumar-jhu-whitehea d-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nHeuristics fo r Network Revenue Management

\nWe consider a network revenue managem ent problem with customer choice and exogenous prices. Such problems are c entral in several applications including airline ticket pricing. Given the infeasibility of explicitly finding optimal policies\, we study the perfo rmance of a class of heuristic policies. These heuristics periodically re- solve the deterministic linear program (DLP) that results when all future random variables are replaced by their average values and implement the so lutions in a probabilistic manner. We provide an upper bound for the expec ted revenue loss under such policies when compared to the optimal policy. Using this bound\, we construct a schedule of re-solving times such that t he resulting expected revenue loss is bounded by a constant that is indepe ndent of the size of the problem.

\nJoint work with Stefanus Jasin a t University of Michigan.

\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-7408@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Energy Prices & Dynamic Games with Stochastic Demand\nThe drama tic decline in oil prices\, from around $110 per barrel in June 2014 to ar ound $30 in January 2016 highlights the importance of competition between different energy producers. Indeed\, the price drop has been primarily at tributed to OPEC’s strategic decision (until very recently) not to curb it s oil production in the face of increased supply of shale gas and oil in t he US\, which was spurred by the development of fracking technology. Most dynamic Cournot models focus on supply-side factors\, such as increased sh ale oil\, and random discoveries. However declining and uncertain demand f rom China is a major factor driving oil price volatility. We study Cournot games in a stochastic demand environment\, and present asymptotic and num erical results\, as well as a modified Hotelling’s rule for games with sto chastic demand. DTSTART;TZID=America/New_York:20170403T103000 DTEND;TZID=America/New_York:20170403T113000 SEQUENCE:0 SUMMARY:Seminar: Ronnie Sircar (Princeton University) @ Ames 234 URL:https://engineering.jhu.edu/ams/events/seminar-ronnie-sircar-princeton- university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Energy Prices & Dynamic Games with Stochastic Demand

\nThe dramatic decline in oi l prices\, from around $110 per barrel in June 2014 to around $30 in Janua ry 2016 highlights the importance of competition between different energy producers. Indeed\, the price drop has been primarily attributed to OPEC’ s strategic decision (until very recently) not to curb its oil production in the face of increased supply of shale gas and oil in the US\, which was spurred by the development of fracking technology. Most dynamic Cournot m odels focus on supply-side factors\, such as increased shale oil\, and ran dom discoveries. However declining and uncertain demand from China is a ma jor factor driving oil price volatility. We study Cournot games in a stoch astic demand environment\, and present asymptotic and numerical results\, as well as a modified Hotelling’s rule for games with stochastic demand.\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9310@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20170405T150000 DTEND;TZID=America/New_York:20170405T160000 SEQUENCE:0 SUMMARY:Data Seminar: Afonso Bandeira (NYU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/data-seminar-afonso-bandeira-nyu -whitehead-304/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-9478@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20170405T150000 DTEND;TZID=America/New_York:20170405T160000 SEQUENCE:0 SUMMARY:HUSAM Presents Coffee Chat URL:https://engineering.jhu.edu/ams/events/husam-presents-coffee-chat-2/ X-COST-TYPE:free X-WP-IMAGES-URL:thumbnail\;https://engineering.jhu.edu/ams/wp-content/uploa ds/sites/44/2017/03/Coffee_Chat-002-300x139.jpg\;300\;139\,medium\;https:/ /engineering.jhu.edu/ams/wp-content/uploads/sites/44/2017/03/Coffee_Chat-0 02-300x139.jpg\;300\;139\,large\;https://engineering.jhu.edu/ams/wp-conten t/uploads/sites/44/2017/03/Coffee_Chat-002-300x139.jpg\;300\;139\,full\;ht tps://engineering.jhu.edu/ams/wp-content/uploads/sites/44/2017/03/Coffee_C hat-002-300x139.jpg\;300\;139 X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

\nConstructing gene regula tory networks is a fundamental task in systems biology. We introduce a Gau ssian reciprocal graphical model for inference about gene regulatory relat ionships by integrating mRNA gene expression and DNA level information inc luding copy number and methylation. Data integration allows for inference on the directionality of certain regulatory relationships\, which would be otherwise indistinguishable due to Markov equivalence. Efficient inferenc e is developed based on simultaneous equation models. Bayesian model selection techniques are adopted to estimate t he graph structure. We illustrate our approach by simulations and two appl ications in ZODIAC pairwise gene interaction analysis and colon adenocarci noma pathway analysis.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-8444@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:From solving PDEs to machine learning PDEs: \nAn odyssey in com putational mathematics\nGeorge Em Karniadakis\nDivision of Applied Mathema tics\, Brown University\nAbstract: In the last 30 years I have pursued the numerical solution of partial differential equations (PDEs) using spectra l and spectral elements methods for diverse applications\, starting from d eterministic PDEs in complex geometries\, to stochastic PDEs for uncertain ty quantification\, and to fractional PDEs that describe non-local behavio r in disordered media and viscoelastic materials. More recently\, I have b een working on solving PDEs in a fundamentally different way. I will prese nt a new paradigm in solving linear and nonlinear PDEs from noisy measurem ents without the use of the classical numerical discretization. Instead\, we infer the solution of PDEs from noisy data\, which can represent measur ements of variable fidelity. The key idea is to encode the structure of th e PDE into prior distributions and train Bayesian nonparametric regression models on available noisy data. The resulting posterior distributions can be used to predict the PDE solution with quantified uncertainty\, efficie ntly identify extrema via Bayesian optimization\, and acquire new data via active learning. Moreover\, I will present how we can use this new framew ork to learn PDEs from noisy measurements of the solution and the forcing terms.\n \nBio: George Karniadakis received his S.M. and Ph.D. from Massac husetts Institute of Technology. He was appointed Lecturer in the Departme nt of Mechanical Engineering at MIT in 1987 and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princet on University as Assistant Professor in the Department of Mechanical and A erospace Engineering and as Associate Faculty in the Program of Applied an d Computational Mathematics. He was a Visiting Professor at Caltech in 199 3 in the Aeronautics Department and joined Brown University as Associate P rofessor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996\, he continues to be a Visiting P rofessor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is a Fellow of the Society for Industrial and Applied Mathematics (SIAM\, 20 10-)\, Fellow of the American Physical Society (APS\, 2004-)\, Fellow of t he American Society of Mechanical Engineers (ASME\, 2003-) and Associate F ellow of the American Institute of Aeronautics and Astronautics (AIAA\, 20 06-). He received the Ralf E Kleinman award from SIAM (2015)\, the J. Tins ley Oden Medal (2013)\, and the CFD award (2007) by the US Association in Computational Mechanics. His h-index is 79 and he has been cited over 32\, 500 times.\n DTSTART;TZID=America/New_York:20170420T133000 DTEND;TZID=America/New_York:20170420T143000 SEQUENCE:0 SUMMARY:Seminar: George Karniadakis (Brown University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-george-karniadakis-brown -university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**From
solving PDEs to machine learning PDEs: **

**An odysse
y in computational mathematics**

*George Em Karn
iadakis*

*Division of Applied Mathematics\
, Brown University*

**Abstract:** In the
last 30 years I have pursued the numerical solution of partial differenti
al equations (PDEs) using spectral and spectral elements methods for diver
se applications\, starting from deterministic PDEs in complex geometries\,
to stochastic PDEs for uncertainty quantification\, and to fractional PDE
s that describe non-local behavior in disordered media and viscoelastic ma
terials. More recently\, I have been working on solving PDEs in a fundamen
tally different way. I will present a new paradigm in solving linear and n
onlinear PDEs from noisy measurements without the use of the classical num
erical discretization. Instead\, we infer the solution of PDEs from noisy
data\, which can represent measurements of variable fidelity. The key idea
is to encode the structure of the PDE into prior distributions and train
Bayesian nonparametric regression models on available noisy data. The resu
lting posterior distributions can be used to predict the PDE solution with
quantified uncertainty\, efficiently identify extrema via Bayesian optimi
zation\, and acquire new data via active learning. Moreover\, I will prese
nt how we can use this new framework to learn PDEs from noisy measurements
of the solution and the forcing terms.

\n

**Bio: George Karniadakis received his S.M. and Ph.D. from Massachusetts In
stitute of Technology. He was appointed Lecturer in the Department of Mech
anical Engineering at MIT in 1987 and subsequently he joined the Center fo
r Turbulence Research at Stanford / Nasa Ames. He joined Princeton Univers
ity as Assistant Professor in the Department of Mechanical and Aerospace E
ngineering and as Associate Faculty in the Program of Applied and Computat
ional Mathematics. He was a Visiting Professor at Caltech in 1993 in the A
eronautics Department and joined Brown University as Associate Professor o
f Applied Mathematics in the Center for Fluid Mechanics in 1994. After bec
oming a full professor in 1996\, he continues to be a Visiting Professor a
nd Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is a Fellow
of the Society for Industrial and Applied Mathematics (SIAM\, 2010-)\, Fel
low of the American Physical Society (APS\, 2004-)\, Fellow of the America
n Society of Mechanical Engineers (ASME\, 2003-) and Associate Fellow of t
he American Institute of Aeronautics and Astronautics (AIAA\, 2006-). He r
eceived the Ralf E Kleinman award from SIAM (2015)\, the J. Tinsley Oden M
edal (2013)\, and the CFD award (2007) by the US Association in Computatio
nal Mechanics. His h-index is 79 and he has been cited over 32\,500 times.
**

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9298@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Solving Fredholm integrals from incomplete measurements\nWe pre sent an algorithm to solve Fredholm integrals of the first kind with tenso r product structures\, from a limited number of measurements with the goal of using this method to accelerate Nuclear Magnetic Resonance (NMR) acqui sition. This is done by incorporating compressive sampling type arguments to fill in the missing measurements using a priori knowledge of the struct ure of the data. In the first step\, we recover a compressed data matrix f rom measurements that form a tight frame\, and establish that these measur ements satisfy the restricted isometry property (RIP). In the second step\ , we solve the zeroth-order regularization minimization problem using the Venkataramanan-Song-Huerlimann algorithm. We demonstrate the performance o f this algorithm on simulated and real data and we compare it with other s ampling techniques. Our theory applied to both 2D and multidimensional NMR . DTSTART;TZID=America/New_York:20170426T150000 DTEND;TZID=America/New_York:20170426T160000 SEQUENCE:0 SUMMARY:Data Seminar: Wojciech Czaja (University of Maryland\, College Park ) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/data-seminar-wojciech-czaja-univ ersity-maryland-college-park-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Solving Fredholm integrals from incomplete measurements

\nWe p resent an algorithm to solve Fredholm integrals of the first kind with ten sor product structures\, from a limited number of measurements with the go al of using this method to accelerate Nuclear Magnetic Resonance (NMR) acq uisition. This is done by incorporating compressive sampling type argument s to fill in the missing measurements using a priori knowledge of the stru cture of the data. In the first step\, we recover a compressed data matrix from measurements that form a tight frame\, and establish that these meas urements satisfy the restricted isometry property (RIP). In the second ste p\, we solve the zeroth-order regularization minimization problem using th e Venkataramanan-Song-Huerlimann algorithm. We demonstrate the performance of this algorithm on simulated and real data and we compare it with other sampling techniques. Our theory applied to both 2D and multidimensional N MR.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-8416@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Parametrization of discrete optimization problems\, subd eterminants and matrix-decomposition\n \nAbstract:\nThe central goal of th is talk is to identify parameters that explain the complexity of Integer l inear programming defined as follows:\nLet P be a polyhedron. Determine an integral point in P that maximizes a linear function.\n \nIt is obvious t hat the number of integer variables is such a parameter.\nHowever\, in vie w of applications in very high dimensions\, the question emerges whether w e need to treat all variables as integers? In other words\, can we reparam etrize integer programs with significantly less integer variables?\n \nA s econd much less obvious parameter associated with an integer linear progra m is the number Delta defined as the maximum absolute value of any square submatrix of a given integral matrix A with m rows and n columns.\nThis le ads us to the important open question whether we can solve integer linear programming in a polynomial running time in Delta and the instance size?\n \nRegarding the first question\, we exhibit a variety of examples that de monstrate how integer programs can be reformulated using much less integer variables. To this end\, we introduce a generalization of total unimodula rity called the affine TU-dimension of a matrix and study related theory a nd algorithms for determining the affine TU-dimension of a matrix.\n \nReg arding the second question\,\nwe present several new results that illustra te why Delta is an important parameter about the complexity of integer lin ear programs associated with a given matrix A.\nIn particular\, in the non degenerate case integer linear programs with any constant value Delta can be solved in polynomial time.\nThis extends earlier results of Veselov and Chirkov. DTSTART;TZID=America/New_York:20170427T133000 DTEND;TZID=America/New_York:20170427T143000 SEQUENCE:0 SUMMARY:Seminar: Robert Weismantel (ETH Zurich) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-robert-weismantel-eth-zu rich-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Parame trization of discrete optimization problems\, subdeterminants and matrix-d ecomposition

\n\n

Abstract:

\nThe central goal of this talk is to identify parameters that explain the complexity of Integer line ar programming defined as follows:

\nLet P be a polyhedron. Determin e an integral point in P that maximizes a linear function.

\n\n

It is obvious that the number of integer variables is such a parameter.

\nHowever\, in view of applications in very high dimensions\, the q uestion emerges whether we need to treat all variables as integers? In oth er words\, can we reparametrize integer programs with significantly less i nteger variables?

\n\n

A second much less obvious parameter a ssociated with an integer linear program is the number Delta defined as th e maximum absolute value of any square submatrix of a given integral matri x A with m rows and n columns.

\nThis leads us to the important open question whether we can solve integer linear programming in a polynomial running time in Delta and the instance size?

\n\n

Regarding t he first question\, we exhibit a variety of examples that demonstrate how integer programs can be reformulated using much less integer variables. To this end\, we introduce a generalization of total unimodularity called th e affine TU-dimension of a matrix and study related theory and algorithms for determining the affine TU-dimension of a matrix.

\n\n

Reg arding the second question\,

\nwe present several new results that i llustrate why Delta is an important parameter about the complexity of inte ger linear programs associated with a given matrix A.

\nIn particula r\, in the nondegenerate case integer linear programs with any constant va lue Delta can be solved in polynomial time.

\nThis extends earlier r esults of Veselov and Chirkov.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9270@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Vol\, Skew\, and Smile Trading\nAbstract: We use dynamic ally traded portfolios of options to bet on either the quadratic variation of log price\, or on the realized co-variation of log price with log impl ied vol\, or on the quadratic variation of implied vol. Our bets lead to p recise financial meanings for the level\, slope\, and curvature of implied variance in moneyness.\n DTSTART;TZID=America/New_York:20170504T133000 DTEND;TZID=America/New_York:20170504T143000 SEQUENCE:0 SUMMARY:Seminar: Peter Carr (NYU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/seminar-peter-carr-nyu-whitehead -304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Vol\, Skew\, and Smile Tradin g

\nAbstract: We use dynamically traded portfolios of options to bet on either the q uadratic variation of log price\, or on the realized co-variation of log p rice with log implied vol\, or on the quadratic variation of implied vol. Our bets lead to precise financial meanings for the level\, slope\, and curvature of impli ed variance in moneyness.

\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-9294@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT:http://www.math.jhu.edu/~data/ DESCRIPTION: DTSTART;TZID=America/New_York:20170510T150000 DTEND;TZID=America/New_York:20170510T160000 SEQUENCE:0 SUMMARY:Data Seminar: Rachel Ward (University of Texas\, Austin) @ Whitehea d 304 URL:https://engineering.jhu.edu/ams/events/data-seminar-rachel-ward-univers ity-texas-austin-whitehead-304/ X-COST-TYPE:free END:VEVENT BEGIN:VEVENT UID:ai1ec-10353@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: \nTitle: Introduction to the Financial Mathematics Semina r\n \nAbstract:\nThe seminar will have two parts:\nPart I) Daniel Naiman w ill introduce the Financial Mathematics seminar.\nPart II) Sonjala William s will speak about networking and job search strategies. DTSTART;TZID=America/New_York:20170905T133000 DTEND;TZID=America/New_York:20170905T150000 LOCATION:Shaffer 101 SEQUENCE:0 SUMMARY:Financial Mathematics Seminar: Daniel Naiman and Sonjala Williams ( JHU) @ Shaffer 101 URL:https://engineering.jhu.edu/ams/events/introduction-to-the-financial-ma thematics-seminar/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

\n

Tit le: Introduction to the Financial Mathematics Seminar

\n\n

Abstract:

\nThe seminar will have two parts:

\nPart I) Dani el Naiman will introduce the Financial Mathematics seminar.

\nPart I I) Sonjala Williams will speak about networking and job search strategies.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10398@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Dr. Dave Schrader will be speaking at Sports Analytics Club’s f irst meeting of the year on Monday\, September 11 at 7pm in Schafer 3. Dr. Schrader is an expert in the field of sports analytics\, and has a wide b readth of experience in industry and in speaking to college students about the field. Individuals with all levels of experience in sports analytics are welcome to attend\, and the talk should give a good overview of how sp orts analytics are currently being used and how students can get involved themselves.\n \nThe Dr. Schrader’s talk is entitled “The Golden Age of Spo rts Analytics\,” and it will cover the following topics:\n\nWhat’s happeni ng around the world to collect and analyze data for recruiting\, player de velopment\, game planning\, and injury prevention?\nHow are analytics bein g used to evaluate and improve business operations – ticket pricing\, sal es\, sponsorships?\nWhat analytics are the leading pro teams and leagues u sing for basketball\, baseball\, football\, hockey\, and soccer?\nHow quic kly are analytics being adopted at the college level? Who is leading? What are they doing?\nHow can other parts of the university\, like the busines s school or computer science departments\, collaborate with sports program s to provide analytics for teams? What are good “Moneyball” projects to l aunch? What have other schools done?\nWhere can you get more information? What to read? What conferences to attend? DTSTART;TZID=America/New_York:20170911T190000 DTEND;TZID=America/New_York:20170911T203000 SEQUENCE:0 SUMMARY:Dr. Dave Schrader at Sports Analytics Club Meeting @ 7pm in Schafer 3 URL:https://engineering.jhu.edu/ams/events/dr-dave-schrader-at-sports-analy tics-club-meeting-7pm-in-schafer-3/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nDr. Dave Schr ader will be speaking at Sports Analytics Club’s first meeting of the year on Monday\, September 11 at 7pm in Schafer 3. Dr. Schrader is an expert i n the field of sports analytics\, and has a wide breadth of experience in industry and in speaking to college students about the field. Individuals with all levels of experience in sports analytics are welcome to attend\, and the talk should give a good overview of how sports analytics are curre ntly being used and how students can get involved themselves.

\n\n

The Dr. Schrader’s talk is entitled “The Golden Age of Sports Analyt ics\,” and it will cover the following topics:

\n- \n
- What’s happe ning around the world to collect and analyze data for recruiting\, player development\, game planning\, and injury prevention? \n
- How are ana lytics being used to evaluate and improve business operations – ticket pr icing\, sales\, sponsorships? \n
- What analytics are the leading pro teams and leagues using for basketball\, baseball\, football\, hockey\, a nd soccer? \n
- How quickly are analytics being adopted at the colleg e level? Who is leading? What are they doing? \n
- How can other part s of the university\, like the business school or computer science departm ents\, collaborate with sports programs to provide analytics for teams? W hat are good “Moneyball” projects to launch? What have other schools done ? \n
- Where can you get more information? What to read? What confere nces to attend? \n

Title: No equ ations\, no variables\, no parameters\, no space\, no time: Data and the c omputational modeling of complex/multiscale systems

\nAbstract: Obta ining predictive dynamical equations from data lies at the heart of scienc e and engineering modeling\, and is the linchpin of our technology. In mat hematical modeling one typically progresses from observations of the world (and some serious thinking!) first to equations for a model\, and then to the analysis of the model to make predictions. Good mathematical models g ive good predictions (and inaccurate ones do not) – but the computational tools for analyzing them are the same: algorithms that are typically based on closed form equations. While the skeleton of the process remains the s ame\, today we witness the development of mathematical techniques that ope rate directly on observations -data-\, and appear to circumvent the seriou s thinking that goes into selecting variables and parameters and deriving accurate equations. The process then may appear to the user a little like making predictions by “looking in a crystal ball”. Yet the “serious thinki ng” is still there and uses the same -and some new- mathematics: it goes i nto building algorithms that “jump directly” from data to the analysis of the model (which is now not available in closed form) so as to make predic tions. Our work here presents a couple of efforts that illustrate this “ne w” path from data to predictions. It really is the same old path\, but it is travelled by new means.

\nRelated papers:

\nParsimonious Re
presentation of Nonlinear Dynamical Systems through Manifold Learning: a C
hemotaxis Case Study

\nAn Equal Space for Complex Data with Unknown I
nternal Order: Observability\, Gauge Invariance and Manifold Learning Kev
rekidis

Symmetry\, Temporal Information\, and Succinct Representatio n of Random Graph Structures

\nI will discuss mathematical aspects o f my recent work on two related problems at the intersection of random gra phs and information theory: (i) node order inference – for a dynamic rando m graph model\, determine the extent to which the order in which nodes arr ived can be inferred from the graph structure\, and (ii) source coding of structures – for a given graph model\, exhibit an efficiently computable a nd invertible mapping from unlabeled graphs to bit strings with minimum po ssible expected code length. Both problems are connected to the study of t he symmetries of the graph model\, as well as another combinatorial quanti ty – the typical number of feasible labeled representatives of a given str ucture. I will focus on the case of the preferential attachment model\, fo r which we are able to give a (nearly) complete characterization of the be havior of the size of the automorphism group\, as well as a provably asymp totically optimal algorithm for (ii)\, and optimal estimators for certain natural formulations of (i).

\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10414@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Semiparametric spectral modeling of the Drosophila conn ectome\nAbstract: We present semiparametric spectral modeling of the comp lete larval Drosophila mushroom body connectome. Motivated by a thorough e xploratory data analysis of the network via Gaussian mixture modeling (GMM ) in the adjacency spectral embedding (ASE) representation space\, we intr oduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model\, and i s amenable to semiparametric GMM in the ASE representation space. The resu lting connectome code derived via semiparametric GMM composed with ASE cap tures latent connectome structure and elucidates biologically relevant neu ronal properties. \nRelated papers:\nThe complete connectome of a learning and memory center in an insect brain\nA consistent adjacency spectral emb edding for stochastic blockmodel graphs\nA limit theorem for scaled eigenv ectors of random dot product graphs\nLimit theorems for eigenvectors of th e normalized Laplacian for random graphs DTSTART;TZID=America/New_York:20170920T150000 DTEND;TZID=America/New_York:20170920T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: Carey Priebe (JHU) @ Hodson 203 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-carey-prieb e-jhu-hodson-203/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Title: Semip arametric spectral modeling of the Drosophila connectome

\nAbstract: We present semiparametric spectral modeling of the complete larval Droso phila mushroom body connectome. Motivated by a thorough exploratory data a nalysis of the network via Gaussian mixture modeling (GMM) in the adjacenc y spectral embedding (ASE) representation space\, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a general ization of the stochastic block model (SBM) and a special case of the rand om dot product graph (RDPG) latent position model\, and is amenable to sem iparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent conn ectome structure and elucidates biologically relevant neuronal properties.

\nRelated papers:

\nThe complete connectome of a learning and
memory center in an insect brain

\nA consistent adjacency spectral e
mbedding for stochastic blockmodel graphs

\nA limit theorem for scale
d eigenvectors of random dot product graphs

\nLimit theorems for eige
nvectors of the normalized Laplacian for random graphs

TITLE – On op timizing a submodular utility function

\nABSTRACT – This talk has tw o related parts. Part one is on the maximization of a particular submodula r utility function\, whereas part two is on its minimization. Both problem s arise naturally in combinatorial optimization with risk aversion\, inclu ding estimation of project duration with stochastic task times\, in reliab ility models\, multinomial logit models\, competitive facility location\, combinatorial auctions\, as well as in portfolio optimization.

\nPar t 1: Given a monotone concave univariate function g\, and two vectors c an d d\, we consider the discrete optimization problem of finding a vertex of a polytope maximizing the utility function c’x + g(d’x). The problem is N P-hard for any strictly concave function g even for simple polytopes\, suc h as the uniform matroid\, assignment and path polytopes. We give a 1/2-ap proximation algorithm for it and improvements for special cases\, where g is the square root\, log utility\, negative exponential utility and multin omial logit probability function. In particular\, for the square root func tion\, the approximation ratio improves to 4/5. Although the worst case bo unds are tight\, computational experiments indicate that the approximation algorithm finds solutions within 1-2% optimality gap for most of the inst ances very quickly and can be considerably faster than the existing altern atives.

\nPart 2: We consider a mixed 0-1 conic quadratic optimizati on problem with indicator variables arising in mean-risk optimization. The indicator variables are often used to model non-convexities such as fixed charges or cardinality constraints. Observing that the problem reduces to a submodular function minimization for its binary restriction\, we derive three classes of strong convex valid inequalities by lifting the polymatr oid inequalities on the binary variables. Computational experiments demons trate the effectiveness of the inequalities in strengthening the convex re laxations and\, thereby\, improving the solution times for mean-risk probl ems with fixed charges and cardinality constraints significantly.

\nTitle: Frames — two case studies: ambiguity and uncertainty

\nAbstract: The theor y of frames is an essential concept for dealing with signal representation in noisy environments. We shall examine the theory in the settings of the narrow band ambiguity function and of quantum information theory. For the ambiguity function\, best possible estimates are derived for applicable c onstant amplitude zero autocorrelation (CAZAC) sequences using Weil’s solu tion of the Riemann hypothesis for finite fields. In extending the theory to the vector-valued case modelling multi-sensor environments\, the defini tion of the ambiguity function is characterized by means of group frames. For the uncertainty principle\, Andrew Gleason’s measure theoretic theorem \, establishing the transition from the lattice interpretation of quantum mechanics to Born’s probabilistic interpretation\, is generalized in terms of frames to deal with uncertainty principle inequalities beyond Heisenbe rg’s. My collaborators are Travis Andrews\, Robert Benedetto\, Jeffrey Don atelli\, Paul Koprowski\, and Joseph Woodworth.

\nRelated papers:**\nSuper-resolution by means of Beurling minimal extrapolation\nG
eneralized Fourier frames in terms of balayage\nUncertainty principl
es and weighted norm inequalities\nA frame reconstruction algorithm
with applications to magnetric resonance imaging\nFrame multiplicati
on theory and a vector-valued DFT and ambiguity functions**

Title: An improved approach to calibrating misspecified mathematical models

\nAbstract:

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10421@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Data-driven discovery of governing equations and physica l laws\nAbstract: The emergence of data methods for the sciences in the la st decade has been enabled by the plummeting costs of sensors\, computatio nal power\, and data storage. Such vast quantities of data afford us new o pportunities for data-driven discovery\, which has been referred to as the 4th paradigm of scientific discovery. We demonstrate that we can use emer ging\, large-scale time-series data from modern sensors to directly constr uct\, in an adaptive manner\, governing equations\, even nonlinear dynamic s\, that best model the system measured using modern regression techniques . Recent innovations also allow for handling multi-scale physics phenomeno n and control protocols in an adaptive and robust way. The overall archite cture is equation-free in that the dynamics and control protocols are disc overed directly from data acquired from sensors. The theory developed is d emonstrated on a number of canonical example problems from physics\, biolo gy and engineering. \nRelated papers:\nDiscovering governing equations fro m data by sparse identification of nonlinear dynamical systems\nData-drive n discovery of partial differential equations\nChaos as an intermittently forced linear system DTSTART;TZID=America/New_York:20171004T150000 DTEND;TZID=America/New_York:20171004T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: Nathan Kutz (University of Washington) @ Hods on 203 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-nathan-kutz -university-washington-hodson-203/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Title: Data-d riven discovery of governing equations and physical laws

\nAbstract: The emergence of data methods for the sciences in the last decade has bee n enabled by the plummeting costs of sensors\, computational power\, and d ata storage. Such vast quantities of data afford us new opportunities for data-driven discovery\, which has been referred to as the 4th paradigm of scientific discovery. We demonstrate that we can use emerging\, large-scal e time-series data from modern sensors to directly construct\, in an adapt ive manner\, governing equations\, even nonlinear dynamics\, that best mod el the system measured using modern regression techniques. Recent innovati ons also allow for handling multi-scale physics phenomenon and control pro tocols in an adaptive and robust way. The overall architecture is equation -free in that the dynamics and control protocols are discovered directly f rom data acquired from sensors. The theory developed is demonstrated on a number of canonical example problems from physics\, biology and engineerin g.

\nRelated papers:

\nDiscovering governing equations from da
ta by sparse identification of nonlinear dynamical systems

\nData-dri
ven discovery of partial differential equations

\nChaos as an intermi
ttently forced linear system

Title: Multid imensional wavelet signal denoising via adaptive random partitioning

\n\n

Abstract: Traditional statistical wavelet analysis usually fo cuses on modeling the wavelet coefficients under a given\, predetermined w avelet transform. Such analysis may quickly lose efficiency in multivariat e problems under traditional multivariate wavelet transforms\, which are s ymmetric with respect to the dimensions\, as predetermined wavelet transfo rms cannot adaptively exploit the energy distribution in a problem-specifi c manner. We introduce a principled probabilistic framework for incorporat ing such adaptivity by (i) representing multivariate functions using one-d imensional (1D) wavelet transforms applied to a permuted version of the or iginal function\, and (ii) placing a hyperprior on the corresponding permu tation. Such a representation can achieve substantially better energy conc entration in the wavelet coefficients and highly scalable inference algori thms. In particular\, when combined with the Haar basis\, we obtain the ex act Bayesian inference analytically through a recursive message passing al gorithm with a computational complexity that scales linearly with sample s ize. In addition\, we propose a sequential Monte Carlo (SMC) inference alg orithm for other wavelet bases using the exact Haar solution as the propos al. We demonstrate that with this framework even simple 1D Haar wavelets c an achieve excellent performance in both 2D and 3D image reconstruction vi a numerical experiments\, outperforming state-of-the-art multidimensional wavelet-based methods especially in low signal-to-noise ratio settings\, a t a fraction of the computational cost.

\n\n

This is a joint work with Li Ma.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10566@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Discrete Nonlinear Optimization by State-Space Decomposi tions\n \nAbstract: In this talk we will discuss a decomposition approach for binary optimization problems with nonlinear objectives and linear cons traints. Our methodology relies on the partition of the objective function into separate low-dimensional dynamic programming (DP) models\, each of w hich can be equivalently represented as a shortest-path problem in an unde rlying state transition graph. We show that the associated transition grap hs can be related by a mixed-integer linear program (MILP) so as to produc e exact solutions to the original nonlinear problem. To address DPs with l arge state spaces\, we present a general relaxation mechanism which dynami cally aggregates states during the construction of the transition graphs. The resulting MILP provides both lower and upper bounds to the nonlinear f unction\, and may be embedded in branch-and-bound procedures to find prova bly optimal solutions. We describe how to specialize our technique for str uctured objectives (e.g.\, submodular functions) and consider three proble ms arising in revenue management\, portfolio optimization\, and healthcare . Numerical studies indicate that the proposed technique often outperforms state-of-the-art approaches by orders of magnitude in these applications. DTSTART;TZID=America/New_York:20171006T100000 DTEND;TZID=America/New_York:20171006T110000 SEQUENCE:0 SUMMARY:Additional AMS Seminar: David Bergman (UConn) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/additional-ams-seminar-david-ber gman-uconn-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n* T
itle*: Discrete Nonlinear Optimization by State-Space Decompo
sitions

\n

* Abstract*: In this talk
we will discuss a decomposition approach for binary optimization problems
with nonlinear objectives and linear constraints. Our methodology relies
on the partition of the objective function into separate low-dimensional d
ynamic programming (DP) models\, each of which can be equivalently represe
nted as a shortest-path problem in an underlying state transition graph. W
e show that the associated transition graphs can be related by a mixed-int
eger linear program (MILP) so as to produce exact solutions to the origina
l nonlinear problem. To address DPs with large state spaces\, we present a
general relaxation mechanism which dynamically aggregates states during t
he construction of the transition graphs. The resulting MILP provides both
lower and upper bounds to the nonlinear function\, and may be embedded in
branch-and-bound procedures to find provably optimal solutions. We descri
be how to specialize our technique for structured objectives (e.g.\, submo
dular functions) and consider three problems arising in revenue management
\, portfolio optimization\, and healthcare. Numerical studies indicate tha
t the proposed technique often outperforms state-of-the-art approaches by
orders of magnitude in these applications.

**Title
:** Discussion of quantitative careers in private banking

\n

**Abstract:** TBA

Title: Data a ssimilation with stochastic model reduction

\nAbstract: In weather a nd climate prediction\, data assimilation combines data with dynamical mod els to make prediction\, using ensemble of solutions to represent the unce rtainty. Due to limited computational resources\, reduced models are neede d and coarse-grid models are often used\, and the effects of the subgrid s cales are left to be taken into account. A major challenge is to account f or the memory effects due to coarse graining while capturing the key stati stical-dynamical properties. We propose to use nonlinear autoregression mo ving average (NARMA) type models to account for the memory effects\, and d emonstrate by examples that the resulting NARMA type stochastic reduced mo dels can capture the key statistical and dynamical properties and therefor e can improve the performance of ensemble prediction in data assimilation. The examples include the Lorenz 96 system (which is a simplified model of the atmosphere) and the Kuramoto-Sivashinsky equation of spatiotemporally chaotic dynamics.

\nRelated papers:

\nDiscrete approach to st
ochastic parametrization and dimension reduction in nonlinear dynamics

\nAccounting for model error from unresolved scales in ensemble Kalman f
ilters by stochastic parametrization

**Title
: **Financial Contagion and Systemic Risk

**Abstract:
**Financial contagion occurs when the distress of one bank jeopard
izes the health of other financial firms\, and can ultimately spread to th
e real economy. The spread of defaults in the financial system can occur d
ue to both local connections\, e.g.\, contractual obligations\, and global
connections\, e.g.\, through the prices of assets due to mark-to-market v
aluation. As evidenced by the 2007-2009 financial crisis\, the cost of a s
ystemic event is tremendous\, thus requiring a detailed look at the contri
buting factors. In this talk\, we will detail the local contagion model of
Eisenberg and Noe (2001). However\, in utilizing this model\, central ban
kers and regulators often must estimate the interbank liabilities because
complete information on bilateral obligations is rarely available. This es
timation can introduce errors to the level of financial contagion and risk
in the system. We will consider a sensitivity analysis of the Eisenberg-N
oe model to determine the size of these potential estimation errors.

Title: “Hyper -Molecules” in Cryo-Electron Microscopy (cryo-EM)

\nAbstract: Cryo-E M is an imaging technology that is revolutionizing structural biology\; th e Nobel Prize in Chemistry 2017 was recently awarded to Jacques Dubochet\, Joachim Frank and Richard Henderson “for developing cryo-electron microsc opy for the high-resolution structure determination of biomolecules in sol ution”. Cryo-electron microscopes produce a large number of very noisy two -dimensional projection images of individual frozen molecules. Unlike rela ted tomography methods\, such as computed tomography (CT)\, the viewing di rection of each image is unknown. The unknown directions\, together with e xtreme levels of noise and additional technical factors\, make the determi nation of the structure of molecules challenging. Unlike other structure d etermination methods\, such as x-ray crystallography and nuclear magnetic resonance (NMR)\, cryo-EM produces measurements of individual molecules an d not ensembles of molecules. Therefore\, cryo-EM could potentially be use d to study mixtures of different conformations of molecules. While current algorithms have been very successful at analyzing homogeneous samples\, a nd can recover some distinct conformations mixed in solutions\, the determ ination of multiple conformations\, and in particular\, continuums of simi lar conformations (continuous heterogeneity)\, remains one of the open pro blems in cryo-EM. I will discuss the “hyper-molecules” approach to continu ous heterogeneity\, and the numerical tools and analysis methods that we a re developing in order to recover such hyper-molecules.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10484@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Optimization with Polyhedral Constraints\n \nAbstract:\n A two-phase strategy is presented for solving an optimization problem whos e feasible set is a polyhedron. Phase one is the gradient projection algor ithm\, while phase two is essentially any algorithm for solving a linearly constrained optimization problem. Using some simple rules for branching b etween the two phases\, it is shown\, under suitable assumptions\, that on ly the linearly constrained optimization algorithm is performed asymptotic ally. Hence\, the asymptotic convergence behavior of the two phase algorit hm coincides with the convergence behavior of the linearly constrained opt imizer. Numerical results are presented using CUTE test problems\, a recen tly developed algorithm for projecting a point onto a polyhedron\, and the conjugate gradient algorithm for the linearly constrained optimizer. DTSTART;TZID=America/New_York:20171019T133000 DTEND;TZID=America/New_York:20171019T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Bill Hager (University of Florida) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-bill-hager-universit y-florida-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Optimi zation with Polyhedral Constraints

\n\n

Abstract:

\nA t wo-phase strategy is presented for solving an optimization problem whose f easible set is a polyhedron. Phase one is the gradient projection algorith m\, while phase two is essentially any algorithm for solving a linearly co nstrained optimization problem. Using some simple rules for branching betw een the two phases\, it is shown\, under suitable assumptions\, that only the linearly constrained optimization algorithm is performed asymptoticall y. Hence\, the asymptotic convergence behavior of the two phase algorithm coincides with the convergence behavior of the linearly constrained optimi zer. Numerical results are presented using CUTE test problems\, a recently developed algorithm for projecting a point onto a polyhedron\, and the co njugate gradient algorithm for the linearly constrained optimizer.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10432@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:TBA DTSTART;TZID=America/New_York:20171025T150000 DTEND;TZID=America/New_York:20171025T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: John Harlim (Penn State University) @ Hodson 203 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-john-harlim -penn-state-university-hodson-203/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTBA

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10487@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Coherence in Statistical Modeling of Networks\nAbstract :\nGeorge Box famously said\, “All models are wrong\, but some are useful. ”\nClassic texts define a statistical model as “a set of distributions on the sample space” (Cox and Hinkley\, 1976\; Lehman\, 1983\; Barndorff-Niel son and Cox\, Bernardo and Smith\, 1994).\nMotivated by some longstanding questions in the analysis of network data\, I will examine both of these s tatements\, first from a general point of view\, and then in the context o f some recent developments in network analysis.\nThe confusion caused by t hese statements is clarified by the realization that the definition of sta tistical model must be refined — it must be more than just a set. With th is\, the ambiguity in Box’s statement — e.g.\, what determines whether a m odel is ‘wrong’ or ‘useful’? — can be clarified by a logical property that I call ‘coherence’. After clarification\, a model is deemed useful as lo ng as it is coherent\, i.e.\, inferences from it ‘make sense’.\nI will the n discuss some implications for the statistical modeling of network data. DTSTART;TZID=America/New_York:20171026T133000 DTEND;TZID=America/New_York:20171026T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Harry Crane ( Rutgers University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-harry-crane-rutgers- university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Coher ence in Statistical Modeling of Networks

\nAbstract:

\nGeorge Box famously said\, “All models are wrong\, but some are useful.”

\nClassic texts define a statistical model as “a set of distributions on the sample space” (Cox and Hinkley\, 1976\; Lehman\, 1983\; Barndorff-Nielson and Cox\, Bernardo and Smith\, 1994).

\nMotivated by some longstand ing questions in the analysis of network data\, I will examine both of the se statements\, first from a general point of view\, and then in the conte xt of some recent developments in network analysis.

\nThe confusion caused by these statements is clarified by the realization that the defini tion of statistical model must be refined — it must be more than just a se t. With this\, the ambiguity in Box’s statement — e.g.\, what determines whether a model is ‘wrong’ or ‘useful’? — can be clarified by a logical pr operty that I call ‘coherence’. After clarification\, a model is deemed u seful as long as it is coherent\, i.e.\, inferences from it ‘make sense’.< /p>\n

I will then discuss some implications for the statistical modeling of network data.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10680@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Functional central limit theorems for rough volatility m odels\nAbstract: We extend Donsker’s approximation of Brownian motion to f ractional Brownian motion with any Hurst exponent (including the ’rough’ c ase H < 1/2)\, and Volterra-like processes. Some of the most relevant cons equences of our ‘rough Donsker (rDonsker) Theorem’ are convergence results (with rates) for discrete approximations of a large class of rough models . This justifies the validity of simple and easy-to-implement Monte-Carlo methods\, for which we provide detailed numerical recipes. We test these a gainst the current benchmark hybrid scheme of Bennedsen\, Lunde\, and Pakk anen and find remarkable agreement (for a large range of values of H). Thi s rDonsker Theorem further provides a weak convergence proof for the hybri d scheme itself\, and allows to construct binomial trees for rough volatil ity models\, the first available scheme (in the rough volatility context) for early exercise options such as American or Bermudan. The talk is based on joint work with B. Horvath and A. Muguruza.\n DTSTART;TZID=America/New_York:20171031T133000 DTEND;TZID=America/New_York:20171031T150000 SEQUENCE:0 SUMMARY:Financial Mathematics Seminar: Dr. Antoine Jacquier (Imperial Colle ge London) URL:https://engineering.jhu.edu/ams/events/financial-mathematics-seminar-dr -antoine-jacquier-imperial-college-london/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Functional central limit theorems for rough volatility models\n

**Abstract: **We extend Donsker’s approximation of Bro
wnian motion to fractional Brownian motion with any Hurst exponent (includ
ing the ’rough’ case H < 1/2)\, and Volterra-like processes. Some of the m
ost relevant consequences of our ‘rough Donsker (rDonsker) Theorem’ are co
nvergence results (with rates) for discrete approximations of a large clas
s of rough models. This justifies the validity of simple and easy-to-imple
ment Monte-Carlo methods\, for which we provide detailed numerical recipes
. We test these against the current benchmark hybrid scheme of Bennedsen\,
Lunde\, and Pakkanen and find remarkable agreement (for a large range of
values of H). This rDonsker Theorem further provides a weak convergence pr
oof for the hybrid scheme itself\, and allows to construct binomial trees
for rough volatility models\, the first available scheme (in the rough vol
atility context) for early exercise options such as American or Bermudan.
The talk is based on joint work with B. Horvath and A. Muguruza.

**<
strong> **

Title: Some m atrix problems in quantum information science

\nAbstract:

\n~~In this talk\, we
present some matrix results and techniques in~~

\nsolving certain opt
imization problems arising in quantum information

\nscience.<
/p>\n

~~No quantum mechanics background is required.~~

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10686@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES;LANGUAGE=en-US:Financial Mathematics Seminar CONTACT: DESCRIPTION:Title: Data scientist at Kensho working focusing on natural lan guage processing DTSTART;TZID=America/New_York:20171107T133000 DTEND;TZID=America/New_York:20171107T150000 SEQUENCE:0 SUMMARY:Financial Mathematics Seminar: Ben Cohen URL:https://engineering.jhu.edu/ams/events/financial-mathematics-seminar-be n-cohen/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
: **Data scientist at Kensho working focusing on natural language p
rocessing

Title: Emerge nt behavior in self-organized dynamics: from consensus to hydrodynamic flo cking

\nAbstract: We discuss several first- and second-order models encountered in opinion and flocking dynamics. The models are driven by dif ferent “rules of engagement”\, which quantify how each member interacts wi th its immediate neighbors. We highlight the role of geometric vs. topolog ical neighborhoods and distinguish between local and global interactions\, while addressing the following two related questions. (i) How local rules of interaction lead\, over time\, to the emergence of consensus\; and (ii ) How the flocking behavior of large crowds captured by their hydrodynamic description.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10494@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Market Efficiency with Micro and Macro Information\nAbs tract:\nWe propose a tractable\, multi-security model in which investors a llocate information processing capacity to acquire micro information about individual stocks and/or macro information about an index fund. Investors solve optimal portfolio selection and information allocation problems. In equilibrium\, all investors are of one of three types: micro informed\, m acro informed\, or uninformed. We investigate the implications for price e fficiency and find an endogenous bias toward micro efficiency: over a rang e of parameter values prices will be more informative about micro than mac ro fundamentals. We explore the model’s implications for the cyclicality o f investor information choices\, for systematic and idiosyncratic return v olatility\, and for excess covariance and volatility. This is joint work w ith Harry Mamaysky.\n\nNo quantum mechanics background is required. DTSTART;TZID=America/New_York:20171109T133000 DTEND;TZID=America/New_York:20171109T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Paul Glasserman (Columbia University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-paul-glasserman-colu mbia-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Marke t Efficiency with Micro and Macro Information

\nAbstract:

\nWe propose a tractable\, multi-security model in which investors allocate in formation processing capacity to acquire micro information about individua l stocks and/or macro information about an index fund. Investors solve opt imal portfolio selection and information allocation problems. In equilibri um\, all investors are of one of three types: micro informed\, macro infor med\, or uninformed. We investigate the implications for price efficiency and find an endogenous bias toward micro efficiency: over a range of param eter values prices will be more informative about micro than macro fundame ntals. We explore the model’s implications for the cyclicality of investor information choices\, for systematic and idiosyncratic return volatility\ , and for excess covariance and volatility. This is joint work with Harry Mamaysky.

\nNo quantum mechanics background is required.\n

\n\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-10663@engineering.jhu.edu/ams
DTSTAMP:20190715T220523Z
CATEGORIES:
CONTACT:
DESCRIPTION:What geometries can we learn from data?\nIn the field of manifo
ld learning\, the foundational theoretical results of Coifman and Lafon (D
iffusion Maps\, 2006) showed that for data sampled near an embedded manifo
ld\, certain graph Laplacian constructions are consistent estimators of th
e Laplace-Beltrami operator on the underlying manifold. Since these operat
ors determine the Riemannian metric\, they completely describe the geometr
y of the manifold (as inherited from the embedding). It was later shown th
at different kernel functions could be used to recover any desired geometr
y\, at least in terms of pointwise estimation of the associated Laplace-Be
ltrami operator. In this talk I will first briefly review the above result
s and then introduce new results on the spectral convergence of these grap
h Laplacians. These results reveal that not all geometries are accessible
in the stronger spectral sense. However\, when the data set is sampled fro
m a smooth density\, there is a natural conformally invariant geometry whi
ch is accessible on all compact manifolds\, and even on a large class of n
on-compact manifolds. Moreover\, the kernel which estimates this geometry
has a very natural construction which we call Continuous k-Nearest Neighbo
rs (CkNN).
DTSTART;TZID=America/New_York:20171115T150000
DTEND;TZID=America/New_York:20171115T160000
SEQUENCE:0
SUMMARY:Data Science Seminar: Tyrus Berry (George Mason University) @ Hodso
n 203
URL:https://engineering.jhu.edu/ams/events/data-science-seminar-tyrus-berry
-george-mason-university-hodson-203/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nWhat geometri es can we learn from data?

\nIn the field of manifold learning\, the foundational theoretical results of Coifman and Lafon (Diffusion Maps\, 2 006) showed that for data sampled near an embedded manifold\, certain grap h Laplacian constructions are consistent estimators of the Laplace-Beltram i operator on the underlying manifold. Since these operators determine the Riemannian metric\, they completely describe the geometry of the manifold (as inherited from the embedding). It was later shown that different kern el functions could be used to recover any desired geometry\, at least in t erms of pointwise estimation of the associated Laplace-Beltrami operator. In this talk I will first briefly review the above results and then introd uce new results on the spectral convergence of these graph Laplacians. The se results reveal that not all geometries are accessible in the stronger s pectral sense. However\, when the data set is sampled from a smooth densit y\, there is a natural conformally invariant geometry which is accessible on all compact manifolds\, and even on a large class of non-compact manifo lds. Moreover\, the kernel which estimates this geometry has a very natura l construction which we call Continuous k-Nearest Neighbors (CkNN).

\n< /BODY> END:VEVENT BEGIN:VEVENT UID:ai1ec-10496@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Transaction Clock\, Stochastic Time Changes and Stochas tic Volatility\nAbstract: The first part of the talk will establish that\, by No Arbitrage\, the log – price process of a stock has to be a time-cha nged Brownian motion under the physical probability measure. Aggregate vol ume and number of trades are empirically tested as possible drivers of the stochastic clock allowing one to recover normality of stock returns.\nThe second part of the talk will show how stochastic volatility can be repres ented through a stochastic time change\, outside the stochastic differenti al equations classically used for volatility in a number of founding model s in Finance. This representation is particularly useful if one wishes to choose a Levy process (outside Brownian motion) for the stock log- price\, as independent increments are contradicted by volatility clustering obser ved in financial markets. The CGMY process with stochastic volatility will be provided as an example. DTSTART;TZID=America/New_York:20171116T133000 DTEND;TZID=America/New_York:20171116T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Helyette Geman (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-eric-xing-carnegie-m ellon-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Transaction Clock\, Stochastic Time Changes and Stochastic Vol
atility

**Abstract**: The first part of the talk will
establish that\, by No Arbitrage\, the log – price process of a stock has
to be a time-changed Brownian motion under the physical probability measu
re. Aggregate volume and number of trades are empirically tested as possib
le drivers of the stochastic clock allowing one to recover normality of st
ock returns.

The second part of the talk will show how stochastic volatility can be represented through a stochastic time change\, outside t he stochastic differential equations classically used for volatility in a number of founding models in Finance. This representation is particularly useful if one wishes to choose a Levy process (outside Brownian motion) fo r the stock log- price\, as independent increments are contradicted by vol atility clustering observed in financial markets. The CGMY process with st ochastic volatility will be provided as an example.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10603@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Join alumni\, faculty\, and students for a Financial Mathematic s alumni reunion\, including speakers\, networking event\, and happy hour. Food and drink will be served. (Informal Happy hour to follow)\n \nRegis ter @ https://jhu.us6.list-manage.com/track/click?u=40512314224886c4ca8b85 6c2&id=836eea02d4&e=1907de3a2c\n \nIf you have any questions\, please cont act Sonjala Williams @ sonjala@jhu.edu\n \n DTSTART;TZID=America/New_York:20171118T120000 DTEND;TZID=America/New_York:20171118T160000 SEQUENCE:0 SUMMARY:JHU Whiting School of Engineering AMS- Financial Math Alumni Reunio n @ Glass Pavilion URL:https://engineering.jhu.edu/ams/events/jhu-whiting-school-engineering-a ms-financial-math-alumni-reunion-glass-pavilion/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nJoin alumni\, faculty\, and students for a Financial Mathematics alumni reunion\, inclu ding speakers\, networking event\, and happy hour. Food and drink will be served. (Informal Happy hour to follow)

\n\n

Register @ https://jhu.us6.list-manage.com/track/click ?u=40512314224886c4ca8b856c2&id=836eea02d4&e=1907de3a2c

\n\n

If you have any questions\, please contact Sonjala Williams @ sonjala@jhu.edu

\n\n

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10664@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:TBA DTSTART;TZID=America/New_York:20171129T150000 DTEND;TZID=America/New_York:20171129T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: Yingzhou Li (Duke University) @ Hodson 203 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-yingzhou-li -duke-university-hodson-203/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

TBA

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10552@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Distributed Synchronization in Engineering Networks\nAbs tract:\nThis talk presents a systematic study of synchronization on distri buted (networked) systems that spans from theoretical modeling and stabili ty analysis to distributed controller design\, implementation and verifica tion. We first focus on developing a theoretical foundation for synchroniz ation of networked oscillators. We study how the interaction type (couplin g) and network configuration (topology) affect the behavior of a populatio n of heterogeneous coupled oscillators. Unlike existing literature that re stricts to specific scenarios\, we show that phase consensus (common phase value) can be achieved for arbitrary network topologies under very genera l conditions on the oscillators’ model.\nWe then focus on more practical a spects of synchronization on computer networks. Unlike existing solutions that tend to rely on expensive hardware to improve accuracy\, we provide a novel algorithm that reduces jitter by synchronizing networked computers without estimating the frequency difference between clocks (skew) or intro ducing offset corrections. We show that a necessary and sufficient conditi on on the network topology for synchronization (in the presence of noise) is the existence of a unique leader in the communication graph. A Linux-ba sed implementation on a cluster of IBM BladeCenter servers experimentally verifies that the proposed algorithm outperforms well-established solution s and that loops can help reduce jitter.\n DTSTART;TZID=America/New_York:20171130T133000 DTEND;TZID=America/New_York:20171130T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Enrique Mallada Garcia (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-enrique-mallada-garc ia-jhu-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Distributed Synchronization in Engineering Networks

This talk presents a systematic study of s ynchronization on distributed (networked) systems that spans from theoreti cal modeling and stability analysis to distributed controller design\, imp lementation and verification. We first focus on developing a theoretical f oundation for synchronization of networked oscillators. We study how the i nteraction type (coupling) and network configuration (topology) affect the behavior of a population of heterogeneous coupled oscillators. Unlike exi sting literature that restricts to specific scenarios\, we show that phase consensus (common phase value) can be achieved for arbitrary network topo logies under very general conditions on the oscillators’ model.

\nWe then focus on more practical aspects of synchronization on computer netwo rks. Unlike existing solutions that tend to rely on expensive hardware to improve accuracy\, we provide a novel algorithm that reduces jitter by syn chronizing networked computers without estimating the frequency difference between clocks (skew) or introducing offset corrections. We show that a n ecessary and sufficient condition on the network topology for synchronizat ion (in the presence of noise) is the existence of a unique leader in the communication graph. A Linux-based implementation on a cluster of IBM Blad eCenter servers experimentally verifies that the proposed algorithm outper forms well-established solutions and that loops can help reduce jitter.

\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10832@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:The Graduate Career Advisor for Financial Math and Applied Math & Statistics will teach some strategies to make the most of the winter af ter fall classes end.\nLearn how to best kick off or revamp your job or in ternship search!\n \n*Food will be served\, grab it 15 minutes before the event!\nRSVP on Handshake @ https://app.joinhandshake.com/events/108603\n \nWalk-ins welcome DTSTART;TZID=America/New_York:20171205T180000 DTEND;TZID=America/New_York:20171205T193000 SEQUENCE:0 SUMMARY:Winter Wonderland: Strategies for Your Winter Job or Internship Sea rch! URL:https://engineering.jhu.edu/ams/events/winter-wonderland-strategies-win ter-job-internship-search/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

The Graduate Career Advisor for Financial Math and Applied Math & Statistics will teach some strategies to make the most of the winter after fall classes end.

\nLearn how to best kick off or revamp your job or internship search!< /p>\n

\n

***Food will be served\, grab it 15 minutes before
the event!**

RSVP on Handshake @ https://app.joinhandshake.com/events/108603

\n\n

Walk-ins welcome

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10444@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:TBA DTSTART;TZID=America/New_York:20171206T150000 DTEND;TZID=America/New_York:20171206T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: Hau-Tieng Wu (Duke University) @ Hodson 203 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-hau-tieng-w u-duke-university-hodson-203/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTBA

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10556@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: The Growing Importance of Satellite Data for Health and Air Quality Applications\n \nAbstract:\nSatellite data are growing in impo rtance for health and air quality end users in the U.S. and around the wor ld. From their “Gods-eye” view\, satellites provide a level of spatial co verage unobtainable by surface monitoring networks. Satellite observation s of various pollutants\, such as nitrogen dioxide and sulfur dioxide\, vi vidly demonstrate the steady improvement of air quality in the U.S. over t he last several decades thanks to environmental regulations\, such as the Clean Air Act. However\, while better\, U.S. air quality is still not at h ealthy levels and there are occasionally extreme events (e.g.\, wildfires\ , toxic spills in Houston after Hurricane Harvey) that expose Americans to high levels of pollution. Satellite data also show that air quality in m any parts of the world is rapidly degrading\, and is likely to continue to do so as the global population is expected to increase by 2 billion by 20 50. In this presentation\, I will discuss the strengths and limitations of current satellite data for health and air quality applications as well as the potential upcoming satellites offer. I will present examples of succe ssful uses of satellite data\, discuss potential uses\, and highlight ongo ing challenges (e.g.\, data processing and visualization) for satellite da ta end users.\n \nBiographical Sketch\nDr. Bryan Duncan is an Earth scient ist at NASA’s Goddard Space Flight Center and has a keen interest in using NASA satellite data for societal benefit\, including for health and air q uality applications. He frequently speaks to representatives of various U. S. and international agencies (e.g.\, World Bank\, UNICEF) about how satel lite data may benefit their objectives and is a member of the NASA Health and Air Quality Applied Sciences Team (HAQAST). He is also the Project Sci entist of the NASA Aura satellite mission\, which has observing air qualit y from space as one of its objectives. DTSTART;TZID=America/New_York:20171207T133000 DTEND;TZID=America/New_York:20171207T143000 SEQUENCE:0 SUMMARY:The John C. & Susan S.G. Wierman Lecture Series: Bryan Duncan (NASA Goddard Space Flight Center) @ Olin 305 URL:https://engineering.jhu.edu/ams/events/john-c-susan-s-g-wierman-lecture -series-bryan-duncan-nasa-goddard-space-flight-center-tba/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: **The Growing Importance of Satellite Data for Health and Air Qua
lity Applications

\n

**Abstract:**

Sat ellite data are growing in importance for health and air quality end users in the U.S. and around the world. From their “Gods-eye” view\, satellite s provide a level of spatial coverage unobtainable by surface monitoring n etworks. Satellite observations of various pollutants\, such as nitrogen dioxide and sulfur dioxide\, vividly demonstrate the steady improvement of air quality in the U.S. over the last several decades thanks to environme ntal regulations\, such as the Clean Air Act. However\, while better\, U.S . air quality is still not at healthy levels and there are occasionally ex treme events (e.g.\, wildfires\, toxic spills in Houston after Hurricane H arvey) that expose Americans to high levels of pollution. Satellite data also show that air quality in many parts of the world is rapidly degrading \, and is likely to continue to do so as the global population is expected to increase by 2 billion by 2050. In this presentation\, I will discuss t he strengths and limitations of current satellite data for health and air quality applications as well as the potential upcoming satellites offer. I will present examples of successful uses of satellite data\, discuss pote ntial uses\, and highlight ongoing challenges (e.g.\, data processing and visualization) for satellite data end users.

\n\n

*Biographical Sketch*

Dr. Bryan Duncan is an Earth sc ientist at NASA’s Goddard Space Flight Center and has a keen interest in u sing NASA satellite data for societal benefit\, including for health and a ir quality applications. He frequently speaks to representatives of variou s U.S. and international agencies (e.g.\, World Bank\, UNICEF) about how s atellite data may benefit their objectives and is a member of the NASA Hea lth and Air Quality Applied Sciences Team (HAQAST). He is also the Project Scientist of the NASA Aura satellite mission\, which has observing air qu ality from space as one of its objectives.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10963@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Principled non-convex optimization for deep learning and phase retrieval\nAbstract: This talk looks at two classes of non-convex p roblems. First\, we discuss phase retrieval problems\, and present a new f ormulation\, called PhaseMax\, that reduces this class of non-convex probl ems into a convex linear program. Then\, we turn our attention to more com plex non-convex problems that arise in deep learning. We’ll explore the no n-convex structure of deep networks using a range of visualization methods . Finally\, we discuss a class of principled algorithms for training “bina rized” neural networks\, and show that these algorithms have theoretical p roperties that enable them to overcome the non-convexities present in neur al loss functions. DTSTART;TZID=America/New_York:20180131T150000 DTEND;TZID=America/New_York:20180131T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: Tom Goldsten (University of Maryland College Park) @ Gilman 219 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-tom-goldste n-university-of-maryland-college-park-gilman-219/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTitle: Princi pled non-convex optimization for deep learning and phase retrieval

\nAbstract: This talk looks at two classes of non-convex problems. First\, we discuss phase retrieval problems\, and present a new formulation\, call ed PhaseMax\, that reduces this class of non-convex problems into a convex linear program. Then\, we turn our attention to more complex non-convex p roblems that arise in deep learning. We’ll explore the non-convex structur e of deep networks using a range of visualization methods. Finally\, we di scuss a class of principled algorithms for training “binarized” neural net works\, and show that these algorithms have theoretical properties that en able them to overcome the non-convexities present in neural loss functions .

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10861@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Approximating Minimal Cut-Generating Functions by Extrem e Functions\nAbstract:\nWith applications in scheduling\, networks\, and g eneralized assignment problems\, integer programs are ubiquitous in a vari ety of engineering disciplines. Often\, integer programming algorithms ma ke use of strategically chosen cutting planes in order to trim the region bounded by the linear constraints without removing any feasible points. Re cently\, there has been a resurgence of interest in the theory of (minimal ) cut generating functions\, as such functions can be used to produce qual ity cuts. Moreover\, the family of minimal functions forms a convex set\; in order to better understand this class of functions\, we wish to study the extreme functions of this set. In this talk\, we shall see that the s et of continuous minimal cut generating functions contains a dense subset of extreme function. DTSTART;TZID=America/New_York:20180201T133000 DTEND;TZID=America/New_York:20180201T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Teresa Lebair (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-tba-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
**: Approximating Minimal Cut-Generating Functions by Extreme Funct
ions

**Abstract**:

\nWith applications in schedu
ling\, networks\, and generalized assignment problems\, integer programs a
re ubiquitous in a variety of engineering disciplines. Often\, integer pr
ogramming algorithms make use of strategically chosen cutting planes in or
der to trim the region bounded by the linear constraints without removing
any feasible points. Recently\, there has been a resurgence of interest in
the theory of (minimal) cut generating functions\, as such functions can
be used to produce quality cuts. Moreover\, the family of minimal functio
ns forms a convex set\; in order to better understand this class of functi
ons\, we wish to study the extreme functions of this set. In this talk\,
we shall see that the set of continuous minimal cut generating functions c
ontains a dense subset of extreme function.

**Title
: ** Limit theorems for eigenvectors of the normalized Laplacian fo
r random graphs

\n

**Abstract:**

“We p rove a central limit theorem for the components of the eigenvectors corres ponding to the d largest eigenvalues of the normalized Laplacian matrix of a finite dimensional random dot product graph. As a corollary\, we show t hat for stochastic blockmodel graphs\, the rows of the spectral embedding of the normalized Laplacian converge to multivariate normals and furthermo re the mean and the covariance matrix of each row are functions of the ass ociated vertex’s block membership. Together with prior results for the eig envectors of the adjacency matrix\, we then compare\, via the Chernoff inf ormation between multivariate normal distributions\, how the choice of emb edding method impacts subsequent inference. We demonstrate that neither em bedding method dominates with respect to the inference task of recovering the latent block assignments.”

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10997@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Monotonicity of optimal contracts without the first-ord er approach\n \nAbstract:\nWe develop a simple sufficient condition for an optimal contract of a moral\nhazard problem to be monotone in the output signal. Existing results on monotonicity\nrequire conditions on the output distribution (namely\, the monotone likelihood ratio\nproperty (MLRP)) an d additional conditions to guarantee that agent’s decision is\napproachabl e via the first-order approach of replacing that\nproblem with its first-o rder conditions. We know of no positive monotonicity\nresults in the setti ng where the first-order approach does not apply. Indeed\, it is\nwell-doc umented that when there are finitely-many possible outputs\, and the\nfirs t-order approach does not apply\, the MLRP alone is insufficient to guaran tee monotonicity.\nHowever\, we show that when there is an interval of pos sible output signals\,\nthe MLRP does suffice to establish monotonicity un der additional technical assumptions\nthat do not guarantee the validity o f the first-order approach.\n \nThis is joint work with Rongzhu Ke (Hong K ong Baptist University). DTSTART;TZID=America/New_York:20180209T090000 DTEND;TZID=America/New_York:20180209T100000 SEQUENCE:0 SUMMARY:Optimization Seminar: Christopher Ryan (University of Chicago) @ Wh itehead 304 URL:https://engineering.jhu.edu/ams/events/optimization-seminar-christopher -ryan-university-chicago-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: **Monotonicity of optimal contracts without the first-order appr
oach

\n

**Abstract:**

We develop a sim ple sufficient condition for an optimal contract of a moral

\nhazard problem to be monotone in the output signal. Existing results on monotoni city

\nrequire conditions on the output distribution (namely\, the m onotone likelihood ratio

\nproperty (MLRP)) and additional condition s to guarantee that agent’s decision is

\napproachable via the first -order approach of replacing that

\nproblem with its first-order con ditions. We know of no positive monotonicity

\nresults in the settin g where the first-order approach does not apply. Indeed\, it is

\nwe ll-documented that when there are finitely-many possible outputs\, and the

\nfirst-order approach does not apply\, the MLRP alone is insuffici ent to guarantee monotonicity.

\nHowever\, we show that when there i s an interval of possible output signals\,

\nthe MLRP does suffice t o establish monotonicity under additional technical assumptions

\nth at do not guarantee the validity of the first-order approach.

\n\n

This is joint work with Rongzhu Ke (Hong Kong Baptist University).\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10863@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Maximum Likelihood Density Estimation under Total Positi vity\n \nAbstract: Nonparametric density estimation is a challenging probl em in theoretical statistics—in general the maximum likelihood estimate (M LE) does not even exist! Introducing shape constraints allows a path forwa rd. This talk offers an invitation to non-parametric density estimation un der total positivity (i.e. log-supermodularity) and log-concavity. Totally positive random variables are ubiquitous in real world data and possess a ppealing mathematical properties. Given i.i.d. samples from such a distrib ution\, we prove that the maximum likelihood estimator under these shape c onstraints exists with probability one. We characterize the domain of the MLE and show that it is in general larger than the convex hull of the obse rvations. If the observations are 2-dimensional or binary\, we show that t he logarithm of the MLE is a tent function (i.e. a piecewise linear functi on) with “poles” at the observations\, and we show that a certain convex p rogram can find it. In the general case the MLE is more complicated. We gi ve necessary and sufficient conditions for a tent function to be concave a nd supermodular\, which characterizes all the possible candidates for the MLE in the general case.\n DTSTART;TZID=America/New_York:20180215T133000 DTEND;TZID=America/New_York:20180215T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Dr. Elina Robeva (MIT) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-tba-whitehead-304-3/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
:** Maximum Likelihood Density Estimation under Total Positivity

\n** **

\n**Abstract:** Nonparametric de
nsity estimation is a challenging problem in theoretical statistics—in gen
eral the maximum likelihood estimate (MLE) does not even exist! Introducin
g shape constraints allows a path forward. This talk offers an invitation
to non-parametric density estimation under total positivity (i.e. log-supe
rmodularity) and log-concavity. Totally positive random variables are ubiq
uitous in real world data and possess appealing mathematical properties. G
iven i.i.d. samples from such a distribution\, we prove that the maximum l
ikelihood estimator under these shape constraints exists with probability
one. We characterize the domain of the MLE and show that it is in general
larger than the convex hull of the observations. If the observations are 2
-dimensional or binary\, we show that the logarithm of the MLE is a tent f
unction (i.e. a piecewise linear function) with “poles” at the observation
s\, and we show that a certain convex program can find it. In the general
case the MLE is more complicated. We give necessary and sufficient conditi
ons for a tent function to be concave and supermodular\, which characteriz
es all the possible candidates for the MLE in the general case.

< /p>\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11000@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: The Learning Premium\nAbstract: We find equilibrium sto ck prices and interest rates in a\nrepresentative-agent model with uncerta in dividends’ growth\, gradually\nrevealed by dividends themselves\, where asset prices are rational –\nreflect current information and anticipate t he impact of future\nknowledge on future prices. In addition to the usual premium for risk\,\nstock returns include a learning premium\, which refle cts the expected\nchange in prices from new information. In the long run\, the learning\npremium vanishes\, as prices and interest rates converge to their\ncounterparts in the standard setting with known growth. The model \nexplains the increase in price-dividend ratios of the past century if\nb oth relative risk aversion and elasticity of intertemporal\nsubstitution a re above one. This is a joint work with Paolo Guasoni. DTSTART;TZID=America/New_York:20180220T133000 DTEND;TZID=America/New_York:20180220T143000 SEQUENCE:0 SUMMARY:Financial Math Seminar: Maxim Bichuch (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/financial-math-seminar-maxim-bic huch-jhu-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
:** The Learning Premium

**Abstract**: We fin
d equilibrium stock prices and interest rates in a

representative- agent model with uncertain dividends’ growth\, gradually

\nrevealed by dividends themselves\, where asset prices are rational –

\nreflec t current information and anticipate the impact of future

\nknowledg e on future prices. In addition to the usual premium for risk\,

\nst ock returns include a learning premium\, which reflects the expected

\nchange in prices from new information. In the long run\, the learning\n

premium vanishes\, as prices and interest rates converge to their\n

counterparts in the standard setting with known growth. The model\n

explains the increase in price-dividend ratios of the past century if

\nboth relative risk aversion and elasticity of intertemporal

\nsubstitution are above one. This is a joint work with Paolo Guasoni.< /p>\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11096@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Data-driven modeling of vector fields and differential f orms by spectral exterior calculus\nAbstract: We discuss a data-driven fra mework for exterior calculus on manifolds. This framework is based on a re presentations of vector fields\, differential forms\, and operators acting on these objects in frames (overcomplete bases) for L^2 and higher-order Sobolev spaces built entirely from the eigenvalues and eigenfunctions of t he Laplacian of functions. Using this approach\, we represent vector field s either as linear combinations of frame elements\, or as operators on fun ctions via matrices. In addition\, we construct a Galerkin approximation s cheme for the eigenvalue problem for the Laplace-de-Rham operator on 1-for ms\, and establish its spectral convergence. We present applications of th is scheme to a variety of examples involving data sampled on smooth manifo lds and the Lorenz 63 fractal attractor. This work is in collaboration wit h Tyrus Berry. DTSTART;TZID=America/New_York:20180221T150000 DTEND;TZID=America/New_York:20180221T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: Dimitris Giannakis (NYU) @ Shaffer 304 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-dimitris-gi annakis-shaffer-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Title: Data-d riven modeling of vector fields and differential forms by spectral exterio r calculus

\nAbstract: We discuss a data-driven framework for exteri or calculus on manifolds. This framework is based on a representations of vector fields\, differential forms\, and operators acting on these objects in frames (overcomplete bases) for L^2 and higher-order Sobolev spaces bu ilt entirely from the eigenvalues and eigenfunctions of the Laplacian of f unctions. Using this approach\, we represent vector fields either as linea r combinations of frame elements\, or as operators on functions via matric es. In addition\, we construct a Galerkin approximation scheme for the eig envalue problem for the Laplace-de-Rham operator on 1-forms\, and establis h its spectral convergence. We present applications of this scheme to a va riety of examples involving data sampled on smooth manifolds and the Loren z 63 fractal attractor. This work is in collaboration with Tyrus Berry.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10630@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Information\, Computation\, Optimization: Connecting the dots in the Traveling Salesman Problem\nAbstract: Few math models scream impossible as loudly as the traveling salesman problem.\nGiven n cities\, the TSP asks for the shortest route to take you to all of them.\nEasy to s tate\, but if P ≠ NP then no solution method can have good asymptotic\nper formance as n goes off to infinity. The popular interpretation is that we simply\ncannot solve realistic examples. But this skips over nearly 70 yea rs of intense\nmathematical study. Indeed\, in 1949 Julia Robinson describ ed the TSP challenge in\npractical terms: “Since there are only a finite n umber of paths to consider\, the\nproblem consists in finding a method for picking out the optimal path when n is\nmoderately large\, say n = 50.” S he went on to propose a linear programming attack\nthat was adopted by her RAND colleagues Dantzig\, Fulkerson\, and Johnson several\nyears later.\n Following in the footsteps of these giants\, we show that a certain tour o f 49\,603\nhistoric sites in the US is shortest possible\, measuring dista nce with point-to-point\nwalking routes obtained from Google Maps. Along t he way\, we discuss the history\,\napplications\, and computation of this fascinating problem.\n \nBiographical Sketch\nWilliam Cook is a University Professor in Combinatorics and Optimization at the University of Waterloo \, where he received his Ph.D. in 1983. Bill\nwas elected a SIAM Fellow i n 2009\, an INFORMS Fellow in 2010\, a member of the National Academy of E ngineering in 2011\, and an American\nMathematics Society Fellow in 2012. He is the author of the popular book In Pursuit of the Traveling Salesman : Mathematics at the Limits of Computation.\nBill is a former Editor-in-Ch ief of the journals Mathematical Programming (Series A and B) and Mathemat ical Programming Computation. He is the past chair and current vice-chair of the Mathematical Optimization Society and a past chair of the INFORMS C omputing Society. DTSTART;TZID=America/New_York:20180222T133000 DTEND;TZID=America/New_York:20180222T143000 SEQUENCE:0 SUMMARY:The Goldman Distinguished Lecture Series: William Cook (University of Waterloo) @ Shaffer 100 URL:https://engineering.jhu.edu/ams/events/ams-seminar-william-cook-univers ity-waterloo-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Information\, Computation\, Optimization: Connecting the dots i
n the Traveling Salesman Problem

**Abstract:** Few ma
th models scream impossible as loudly as the traveling salesman problem.**\nGiven n cities\, the TSP asks for the shortest route to take you to
all of them.\nEasy to state\, but if P ≠ NP then no solution method
can have good asymptotic\nperformance as n goes off to infinity. The
popular interpretation is that we simply\ncannot solve realistic ex
amples. But this skips over nearly 70 years of intense\nmathematical
study. Indeed\, in 1949 Julia Robinson described the TSP challenge in\npractical terms: “Since there are only a finite number of paths to con
sider\, the\nproblem consists in finding a method for picking out th
e optimal path when n is\nmoderately large\, say n = 50.” She went o
n to propose a linear programming attack\nthat was adopted by her RA
ND colleagues Dantzig\, Fulkerson\, and Johnson several\nyears later
.**

Following in the footsteps of these giants\, we show that a cert
ain tour of 49\,603

\nhistoric sites in the US is shortest possible\,
measuring distance with point-to-point

\nwalking routes obtained fro
m Google Maps. Along the way\, we discuss the history\,

\napplication
s\, and computation of this fascinating problem.

\n

*Biographical Sketch*

William Cook is a University Professor in Combinatorics a nd Optimization at the University of Waterloo\, where he received his Ph.D . in 1983. Bill

\nwas e lected a SIAM Fellow in 2009\, an INFORMS Fellow in 2010\, a member of the National Academy of Engineering in 2011\, and an American

\nMathematics Society Fellow in 2012. He is the author of the popular book In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation.

\nBill is a former Editor-in-Chief of the journals Math ematical Programming (Series A and B) and Mathematical Programming Computa tion. He is the past chair and current vice-chair of the Mathematical Opti mization Society and a past chair of the INFORMS Computing Society.

\n< /BODY> END:VEVENT BEGIN:VEVENT UID:ai1ec-10858@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION: Title: PetuumMed: algorithm and system for EHR-based medical decision-making\n \nAbstract:\nWith the rapid growth of electronic health records (EHRs) and the advancement of machine learning technologies\, need s for AI-enabled clinical decision-making support is emerging. In this tal k\, I will present some recent work toward these needs at Petuum Inc. wher e an integrative system that distills insights from large-scale and hetero geneous patient data\, as well as learns and integrates medical knowledge from broader sources such as the literatures and domain experts\, and empo wers medical professionals to make accurate and efficient decisions within the clinical flow\, is being built. I will discuss several aspects of pra ctical clinical decision-support\, such as real-time information extractio n from clinical notes and images\, diagnosis and treatment recommendation\ , automatic report generation and ICD code filling\; and the algorithmic a nd computational challenges behind production-quality solution to these pr oblems. DTSTART;TZID=America/New_York:20180301T133000 DTEND;TZID=America/New_York:20180301T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Eric Xing (Carnegie Mellon) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-eric-xing-carnegie-m ellon-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n** Titl
e: **PetuumMed: algorithm and system for EHR-based medical decisio
n-making

\n

**Abstract:**

With the rap id growth of electronic health records (EHRs) and the advancement of machi ne learning technologies\, needs for AI-enabled clinical decision-making s upport is emerging. In this talk\, I will present some recent work toward these needs at Petuum Inc. where an integrative system that distills insig hts from large-scale and heterogeneous patient data\, as well as learns an d integrates medical knowledge from broader sources such as the literature s and domain experts\, and empowers medical professionals to make accurate and efficient decisions within the clinical flow\, is being built. I will discuss several aspects of practical clinical decision-support\, such as real-time information extraction from clinical notes and images\, diagnosi s and treatment recommendation\, automatic report generation and ICD code filling\; and the algorithmic and computational challenges behind producti on-quality solution to these problems.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11131@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Variance swap DTSTART;TZID=America/New_York:20180306T133000 DTEND;TZID=America/New_York:20180306T143000 SEQUENCE:0 SUMMARY:Financial Math Seminar: John Miller (JHU) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/financial-math-seminar-john-mill er-jhu-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Variance swap

Title: Compar ing relaxations via volume for nonconvex optimization

\nAbstract: Pr actical exact methods for global optimization of mixed-integer nonlinear o ptimization formulations rely on convex relaxation. Then\, one way or anot her (via refinement and/or disjunction)\, global optimality is sought. Suc cess of this paradigm depends on balancing tightness and lightness of rela xations. We will investigate this from a mathematical viewpoint\, comparin g polyhedral relaxations via their volumes. Specifically\, I will present some results concerning: fixed charge problems\, vertex packing in graphs\ , boolean quadratic formulations\, and convexification of monomials in the context of spatial branch-and-bound” for factorable formulations. Our res ults can be employed by users (at the modeling level) and by algorithm des igners/implementers alike.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11106@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:TBA DTSTART;TZID=America/New_York:20180314T150000 DTEND;TZID=America/New_York:20180314T160000 SEQUENCE:0 SUMMARY:Data Science Seminar: Edriss Titi (Texas A&M University) @ Shaffer 304 URL:https://engineering.jhu.edu/ams/events/data-science-seminar-edriss-titi -texas-am-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nTBA

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10867@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Bayesian monotone regression: Rates\, coverage and tests \nAbstract:\nShape restrictions such as monotonicity often naturally arise . In this talk we consider a Bayesian approach to monotone nonparametric r egression with normal error. We assign a prior through piecewise constant functions and impose a conjugate normal prior on the coefficient. Since th e resulting functions need not be monotone\, we project samples from the p osterior on the allowed parameter space to construct a “projection posteri or”. We first obtain contraction rates of the projection posterior distrib utions under various settings. We next obtain the limit posterior distribu tion of a suitably centered and scaled posterior distribution for the func tion value at a point. The limit distribution has some interesting similar ity and difference with the corresponding limit distribution for the maxim um likelihood estimator. By comparing the quantiles of these two distribut ions\, we observe an interesting new phenomenon that coverage of a credibl e interval may be more than the credibility level\, an exact opposite of a phenomenon observed by Cox for smooth regression. We describe a recalibra tion strategy to modify the credible interval to meet the correct level of coverage. Finally we discuss asymptotic properties of Bayes tests for mon otonicity.\nThis talk is based on joint work with Moumita Chakraborty\, a doctoral student at North Carolina State University. DTSTART;TZID=America/New_York:20180315T133000 DTEND;TZID=America/New_York:20180315T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Subhashis Ghoshal (North Carolina State University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-tba-whitehead-304-5/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: **Bayesian monotone regression: Rates\, coverage and tests

Shape restrictions such as monotonici ty often naturally arise. In this talk we consider a Bayesian approach to monotone nonparametric regression with normal error. We assign a prior thr ough piecewise constant functions and impose a conjugate normal prior on t he coefficient. Since the resulting functions need not be monotone\, we pr oject samples from the posterior on the allowed parameter space to constru ct a “projection posterior”. We first obtain contraction rates of the proj ection posterior distributions under various settings. We next obtain the limit posterior distribution of a suitably centered and scaled posterior d istribution for the function value at a point. The limit distribution has some interesting similarity and difference with the corresponding limit di stribution for the maximum likelihood estimator. By comparing the quantile s of these two distributions\, we observe an interesting new phenomenon th at coverage of a credible interval may be more than the credibility level\ , an exact opposite of a phenomenon observed by Cox for smooth regression. We describe a recalibration strategy to modify the credible interval to m eet the correct level of coverage. Finally we discuss asymptotic propertie s of Bayes tests for monotonicity.

\nThis talk is based on joint wor k with Moumita Chakraborty\, a doctoral student at North Carolina State Un iversity.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11155@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Presentation – Career Opportunities at the NSA Targeting AMS\, CS\, ECE\, JHUISI \n \nWhen: Wednesday\, March 28\, 2018 5:30 pm – 8:00 pm EDT\nWhere: Hodson\, 110\, Baltimore\, MD 21218\, United States\nThe Nat ional Security Agency (NSA) currently has opportunities for highly motivat ed researchers to provide expertise\, guidance and support to the developm ent and implementation of mission capabilities that align with mission dri ven challenges.\nThe Advanced Computing Systems Research Program (ACS) at NSA is looking to hire talented researchers for a variety of positions. Th e ACS mission is to collaborate with industry\, academia\, and the governm ent to drive innovative research that will improve advanced computing syst ems for a range of mission applications including cybersecurity\, cryptana lysis\, and complex data analytics. The ACS has significant research proje cts in neuromorphic and probabilistic computing\, novel computer architect ures and technologies\, advanced modeling and simulation\, energy efficien cy\, productivity\, and resilience.\nDr. David J. Mountain will describe o pportunities at NSA\, using his 36 year career as an example. He will also provide an overview of the ACS program and highlight specific research po sitions currently available. This will be followed by an open Q&A.\nDr. Mo untain is the Senior Technical Director at the Laboratory for Physical Sci ences at Research Park\, a Department of Defense research lab in Catonsvil le\, MD. He received a BS in Electrical Engineering from the University of Notre Dame in 1982\, an MS in Electrical Engineering from the University of Maryland\, College Park\, in 1986\, and a PhD in Computer Engineering f rom the University of Maryland\, Baltimore County\, in 2017. His personal research projects have included radiation effects studies\, hot carrier re liability characterization\, and chip-on-flex process development utilizin g ultra-thin circuits. He has been actively involved with 3D electronics r esearch for 25 years and is presently focused on specialized architectures to support advanced neural networks and tensor analysis. Dr. Mountain is the author of more than two dozen papers\, has been awarded eight patents\ , and is a Senior Member of the IEEE.\nStudents RSVP @ https://app.joinhan dshake.com/events/135261\nFaculty and Staff RSVP @ https://www.eventbrite. com/e/career-opportunities-at-the-nsa-targeting-cs-ams-ece-jhuisi-informat ion-session-tickets-43894265931?aff=affiliate1\n\nNote:\n\n*Food will be served at this even during the first 30 minutes\, Please arrive early. The program will begin promptly at 6pm!*\nFor additional information about th is event\, please contact Dr. Antwan D. Clark at aclark66@jhu.edu.\n\n DTSTART;TZID=America/New_York:20180328T173000 DTEND;TZID=America/New_York:20180328T200000 SEQUENCE:0 SUMMARY:Dr. David J. Mountain will present on Career Opportunities at the N SA in Hodson 110 URL:https://engineering.jhu.edu/ams/events/dr-david-j-mountain-will-present -on-career-opportunities-at-the-nsa-in-hodson-110/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Prese
ntation – Career Opportunities at the NSA Targeting AMS\, CS\, ECE\, JHUIS
I **

\n

When: Wednesday\, March 28\, 2018 5:30 pm – 8:00 pm EDT

\nWhere: Hodson\, 110\, Baltimore\, MD 21218\, United St ates

\nThe National Security Agency (NSA) currently has opportuniti es for highly motivated researchers to provide expertise\, guidance and su pport to the development and implementation of mission capabilities that a lign with mission driven challenges.

\nThe Advanced Computing System s Research Program (ACS) at NSA is looking to hire talented researchers fo r a variety of positions. The ACS mission is to collaborate with industry\ , academia\, and the government to drive innovative research that will imp rove advanced computing systems for a range of mission applications includ ing cybersecurity\, cryptanalysis\, and complex data analytics. The ACS ha s significant research projects in neuromorphic and probabilistic computin g\, novel computer architectures and technologies\, advanced modeling and simulation\, energy efficiency\, productivity\, and resilience.

\nDr . David J. Mountain will describe opportunities at NSA\, using his 36 year career as an example. He will also provide an overview of the ACS program and highlight specific research positions currently available. This will be followed by an open Q&A.

\nDr. Mountain is the Senior Technical D irector at the Laboratory for Physical Sciences at Research Park\, a Depar tment of Defense research lab in Catonsville\, MD. He received a BS in Ele ctrical Engineering from the University of Notre Dame in 1982\, an MS in E lectrical Engineering from the University of Maryland\, College Park\, in 1986\, and a PhD in Computer Engineering from the University of Maryland\, Baltimore County\, in 2017. His personal research projects have included radiation effects studies\, hot carrier reliability characterization\, and chip-on-flex process development utilizing ultra-thin circuits. He has be en actively involved with 3D electronics research for 25 years and is pres ently focused on specialized architectures to support advanced neural netw orks and tensor analysis. Dr. Mountain is the author of more than two doze n papers\, has been awarded eight patents\, and is a Senior Member of the IEEE.

\nStudents RSVP @ https://app.joinhandshake.com/events/135261

\nFaculty and Staff RSVP @ https://www.eventbrite.com/e/career-opportunities-a t-the-nsa-targeting-cs-ams-ece-jhuisi-information-session-tickets-43894265 931?aff=affiliate1

\n

\nNote:

\n- \n
- *
**Foo d will be served at this even during the first 30 minutes\, Please arrive early. The program will begin promptly at 6pm!*** \n - For add itional information about this event\, please contact Dr. Antwan D. Clark at aclark66@jhu.edu. \n

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10871@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Real and Artificial Neural Networks\nAbstract: Lately\, just about everybody has been thinking about deep neural networks (DNNs). Do they work? If so\, how? Do they overfit? If not\, why? I will discuss t hese questions and suggest some uncomplicated answers\, at least as a firs t approximation. Turning to biological learning\, I will argue that the st ubborn gap between human and machine performance\, when it comes to interp retation (as opposed to classification)\, can not be substantially closed without architectures that support stronger representations. In particular \, how are we to accommodate the rich collection of spatial and abstract r elationships (‘on’ or ‘inside’\, ‘talking’ or ‘holding hands’\, ‘same’ or ‘different’) that bind parts and objects and define context? I will propos e that the nonlinearities of dendritic integration in real neurons is the missing ingredient in artificial neurons. I will suggest a mechanism for e mbedding relationships in a generative network. DTSTART;TZID=America/New_York:20180329T133000 DTEND;TZID=America/New_York:20180329T143000 SEQUENCE:0 SUMMARY:Duncan Lecture Series- AMS Seminar: Stuart Geman (Brown University) @ Shaffer 100 URL:https://engineering.jhu.edu/ams/events/ams-seminar-stuart-geman-brown-u niversity-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
: **Real and Artificial Neural Networks

**Abstract: <
/strong>Lately\, just about everybody has been thinking about deep neural
networks (DNNs). Do they work? If so\, how? Do they overfit? If not\, why?
I will discuss these questions and suggest some uncomplicated answers\, a
t least as a first approximation. Turning to biological learning\, I will
argue that the stubborn gap between human and machine performance\, when i
t comes to interpretation (as opposed to classification)\, can not be subs
tantially closed without architectures that support stronger representatio
ns. In particular\, how are we to accommodate the rich collection of spati
al and abstract relationships (‘on’ or ‘inside’\, ‘talking’ or ‘holding ha
nds’\, ‘same’ or ‘different’) that bind parts and objects and define conte
xt? I will propose that the nonlinearities of dendritic integration in rea
l neurons is the missing ingredient in artificial neurons. I will suggest
a mechanism for embedding relationships in a generative network.**

**Title
: **Random Walks on Secondary Structure and the Folding of RNA

**Abstract:**

What is the correct characterizati on of the native structure of a protein or a non-coding RNA molecule? Is i t the minimum energy state\, a metastable state\, or a sample from thermal equilibrium? The problem is unsettled and a topic of enduring debate. The “agnostic” approach is through molecular dynamics—make a proper accountin g of the intramolecular forces and interactions with the surrounding liqui d (mostly water)\, write down the corresponding kinetic equation (e.g. a L angevin equation)\, and simulate folding. But this usually isn’t practical \, and approximations need to be made. I will explore an approximation in which the bulk of the folding process is replaced by a random walk\, with discrete moves from one secondary structure to another. An analytic result identifies conditions for guaranteed accuracy. Simulation results\, runni ng the approximation against an optimized integrator of the Langevin equat ion\, achieve the expected accuracy\, but 100 times faster.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11203@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Machine Learning For Health Care\nAbstract:\nWe will pre sent multiple ways in which healthcare data is acquired and machine learni ng methods are currently being introduced into clinical settings.\nThis wi ll include:\n1.)Modeling the prediction of disease\, including Sepsis\, an d ways in which the best treatment decisions for Sepsis patients can be ma de\, from electronic health record (EHR) data using Gaussian processes and deep learning methods.\n2) Predicting surgical complications and transfer learning methods for combining databases\n3) Using mobile apps and integr ated sensors for improving the granularity of recorded health data for chr onic conditions. Current work in these areas will be presented and the fut ure of machine learning contributions to the field will be discussed. DTSTART;TZID=America/New_York:20180404T120000 DTEND;TZID=America/New_York:20180404T130000 SEQUENCE:0 SUMMARY:AMS & BME Presents Speaker: Katherine Heller @ Shaffer 101 URL:https://engineering.jhu.edu/ams/events/ams-bme-presents-speaker-katheri ne-heller-shaffer-101/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Machine Learning For Health Care

**Abstract:**

We will present multiple ways in which healthcare data is acq uired and machine learning methods are currently being introduced into cli nical settings.

\nThis will include:

\n1.)Modeling the predicti
on of disease\, including Sepsis\, and ways in which the best treatment de
cisions for Sepsis patients can be made\, from electronic health record (E
HR) data using Gaussian processes and deep learning methods.

2) Pr edicting surgical complications and transfer learning methods for combinin g databases

\n3) Using mobile apps and integrated sensors for improv ing the granularity of recorded health data for chronic conditions. Curren t work in these areas will be presented and the future of machine learning contributions to the field will be discussed.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10878@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Adaptive Robust Control Under Model Uncertainty\nAbstrac t: We propose a new methodology\, called adaptive robust control\, for so lving a discrete-time Markovian control problem subject to Knightian uncer tainty. We apply the general framework to a financial hedging problem wher e the uncertainty comes from the fact that the true law of the underlying model is only known to belong to a certain family of probability laws. We provide a learning algorithm that reduces the model uncertainty through pr ogressive learning about the unknow system. One of the pillars in the prop osed methodology is the recursive construction of the confidence sets for the unknown parameter. This allows\, in particular\, to establish the corr esponding Bellman system of equations. DTSTART;TZID=America/New_York:20180405T133000 DTEND;TZID=America/New_York:20180405T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Igor Cialenco (Illinois Institute of Technology) @ Whi tehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-igor-cialenco-illino is-institute-technology-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: **Adaptive Robust Control Under Model Uncertainty

**Abstract: **We propose a new methodology\, called adaptive robust
control\, for solving a discrete-time Markovian control problem subject t
o Knightian uncertainty. We apply the general framework to a financial hed
ging problem where the uncertainty comes from the fact that the true law o
f the underlying model is only known to belong to a certain family of prob
ability laws. We provide a learning algorithm that reduces the model uncer
tainty through progressive learning about the unknow system. One of the pi
llars in the proposed methodology is the recursive construction of the con
fidence sets for the unknown parameter. This allows\, in particular\, to e
stablish the corresponding Bellman system of equations.

Mark your calendars fo r the 5th USA Science & Engineering Festival Expo on April 7-8\, 2018! Exp lore 3\,000 hands-on exhibits from the world’s leading scientific and engi neering societies\, universities\, government agencies\, high-tech corpora tions and STEM organizations. The two-day Expo is perfect for children\, t eens\, and families who want to inspire their curious minds.

\n

\n**W
hen: Saturday 10 am- 6 pm and Sunday 10 am- 4 pm **

Join 35 0K+ attendees to celebrate science at the Expo and engage in activities wi th some of the biggest names in STEM. Hear stories of inspiration and cour age\, participate in mind-boggling experiments and rock out to science dur ing our incredible stage shows.

\n**Date<
/strong>: Monday\, April 9th\, 2018\n Time: 7:00 pm:
Enjoy refreshments and snacks with students and faculty.\n7:30 pm:
The Mathemagics performance begins!\nLocation: John
s Hopkins University\, Homewood Campus\; Hodson Hall\, Room 110**

\n

\nOr E-mail us at husam.jhu@gmail.com

\n\n

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10983@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: TBA\n \nAbstract: TBA DTSTART;TZID=America/New_York:20180410T133000 DTEND;TZID=America/New_York:20180410T143000 SEQUENCE:0 SUMMARY:Financial Math Seminar: Sebastien Bossu (NYU Courant & JHU Carey Sc hool) @ Whitehead Hall 304 URL:https://engineering.jhu.edu/ams/events/financial-math-seminar-sebastien -bossu-nyu-courant-jhu-carey-school-whitehead-hall-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
:** TBA

\n

**Abstract:** TBA

**Title
: **Optimal Portfolio under Fractional Stochastic Environment

**Abstract:**

Rough stochastic volatility models h ave attracted a lot of attention recently\, in particular for the linear o ption pricing problem. In this talk\, starting with power utilities\, we p ropose to use a martingale distortion representation of the optimal value function for the nonlinear asset allocation problem in a (non-Markovian) f ractional stochastic environment (for all Hurst index $H \\in (0\, 1)$). W e rigorously establish a first order approximation of the optimal value\, when the return and volatility of the underlying asset are functions of a stationary slowly varying fractional Ornstein-Uhlenbeck process. We prove that this approximation can be also generated by the zeroth order trading strategy providing an explicit strategy which is asymptotically optimal in all admissible controls. Furthermore\, we extend the discussion to genera l utility functions\, and obtain the asymptotic optimality of this strateg y in a specific family of admissible strategies. If time permits\, we will also discuss the problem under fast mean-reverting fractional stochastic environment.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11005@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: On the joint calibration of SPX and VIX options\nAbstrac t: Since VIX options started trading in 2006\, many researchers have attem pted to build a model for the SPX that jointly calibrates to SPX and VIX o ptions. In 2008\, Jim Gatheral showed that a diffusive model could approxi mately\, but not exactly\, fit both markets. Later\, others have argued th at jumps in the SPX were needed to jointly calibrate both markets. We revi sit this problem\, asking the following questions: Does there exist a cont inuous model on the SPX that jointly calibrates to SPX options\, VIX futur es\, and VIX options? If so\, how to build one such model? If not\, why? W e present a novel approach based on the SPX smile calibration condition. I n the limiting case of instantaneous VIX\, the answers are clear and invol ve the timewise convex ordering of two distributions (local variance and instantaneous variance) and a novel application of martingale transport to finance. The real case of a 30-day VIX is more involved\, as time-averagi ng and projection onto a filtration can undo convex ordering. We observe t hat in usual market conditions the distribution of VIX^2 in the local vola tility model and the market-implied distribution of VIX^2 are not in conve x order\, and we show that fast mean-reverting volatility models and rough volatility models are able to reproduce this surprising behavior.\n DTSTART;TZID=America/New_York:20180417T133000 DTEND;TZID=America/New_York:20180417T143000 SEQUENCE:0 SUMMARY:Financial Math Seminar: Dr. Julien Guyon (Columbia & NYU ) @ Whiteh ead 304 URL:https://engineering.jhu.edu/ams/events/financial-math-seminar-dr-julien -guyon-columbia-nyu-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: **On the joint calibration of SPX and VIX options

**Abstract: **Since VIX options started trading in 2006\, many resea
rchers have attempted to build a model for the SPX that jointly calibrates
to SPX and VIX options. In 2008\, Jim Gatheral showed that a diffusive mo
del could approximately\, but not exactly\, fit both markets. Later\, othe
rs have argued that jumps in the SPX were needed to jointly calibrate both
markets. We revisit this problem\, asking the following questions: Does t
here exist a continuous model on the SPX that jointly calibrates to SPX op
tions\, VIX futures\, and VIX options? If so\, how to build one such model
? If not\, why? We present a novel approach based on the SPX smile calibra
tion condition. In the limiting case of instantaneous VIX\, the answers ar
e clear and involve the timewise convex ordering of two distributions (loc
al variance and instantaneous variance) and a novel application of martin
gale transport to finance. The real case of a 30-day VIX is more involved\
, as time-averaging and projection onto a filtration can undo convex order
ing. We observe that in usual market conditions the distribution of VIX^2
in the local volatility model and the market-implied distribution of VIX^2
are not in convex order\, and we show that fast mean-reverting volatility
models and rough volatility models are able to reproduce this surprising
behavior.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10880@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Characterizing the Worst-Case Performance of Algorithms for Nonconvex Optimization\nAbstract: Motivated by various applications\, e.g.\, in data science\, there has been increasing interest in numerical m ethods for minimizing nonconvex functions. Users of such methods often cho ose one algorithm versus another due to worst-case complexity guarantees\, which in contemporary analyses bound the number of iterations required un til a first- or second-order stationarity condition is approximately satis fied. In this talk\, we question whether this is indeed the best manner in which to compare algorithms\, especially since the worst-case behavior of an algorithm is often only seen when it is employed to minimize pedagogic al examples which are quite distinct from functions seen in normal practic e. We propose a new strategy for characterizing algorithms that attempts t o better represent algorithmic behavior in real-world settings. DTSTART;TZID=America/New_York:20180419T133000 DTEND;TZID=America/New_York:20180419T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Frank Curtis (Lehigh University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-frank-curtis-lehigh- university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
: **Characterizing the Worst-Case Performance of Algorithms for Non
convex Optimization

**Abstract: **Motivated by variou
s applications\, e.g.\, in data science\, there has been increasing intere
st in numerical methods for minimizing nonconvex functions. Users of such
methods often choose one algorithm versus another due to worst-case comple
xity guarantees\, which in contemporary analyses bound the number of itera
tions required until a first- or second-order stationarity condition is ap
proximately satisfied. In this talk\, we question whether this is indeed t
he best manner in which to compare algorithms\, especially since the worst
-case behavior of an algorithm is often only seen when it is employed to m
inimize pedagogical examples which are quite distinct from functions seen
in normal practice. We propose a new strategy for characterizing algorithm
s that attempts to better represent algorithmic behavior in real-world set
tings.

**Title
**: Merchant Options of Energy Trading Network

\n

< /p>\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10884@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: “Statistical network modeling via exchangeable interacti on processes”\n \nAbstract:\nMany modern network datasets arise from proce sses of interactions in a population\, such as phone calls\, e-mail exchan ges\, co-authorships\, and professional collaborations. In such interactio n networks\, the interactions comprise the fundamental statistical units\, making a framework for interaction-labeled networks more appropriate for statistical analysis. In this talk\, we present exchangeable interaction n etwork models and explore their basic statistical properties. These models allow for sparsity and power law degree distributions\, both of which are widely observed empirical network properties. I will start by presenting the Hollywood model\, which is computationally tractable\, admits a clear interpretation\, exhibits good theoretical properties\, and performs reaso nably well in estimation and prediction.\nIn many settings\, the series of interactions are structured. E-mail exchanges\, for example\, have a sing le sender and potentially multiple receivers. I will introduce hierarchica l exchangeable interaction models for the study of structured interaction networks. In particular\, I will introduce the Enron model as a canonical example\, which partially pools information via a latent\, shared populati on-level distribution. A detailed simulation study and supporting theoreti cal analysis provide clear model interpretation\, and establish global pow er-law degree distributions. A computationally tractable Gibbs sampling al gorithm is derived. Inference will be shown on the Enron e-mail dataset. I will end with a discussion of how to perform posterior predictive checks on interaction data. Using these proposed checks\, I will show that the m odel fits the data well. DTSTART;TZID=America/New_York:20180426T133000 DTEND;TZID=America/New_York:20180426T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Walter Dempsey (Harvard University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-walter-dempsey-harva rd-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
: **“Statistical network modeling via exchangeable interaction proc
esses”

\n

**Abstract:**

Many modern ne twork datasets arise from processes of interactions in a population\, such as phone calls\, e-mail exchanges\, co-authorships\, and professional col laborations. In such interaction networks\, the interactions comprise the fundamental statistical units\, making a framework for interaction-labeled networks more appropriate for statistical analysis. In this talk\, we pre sent exchangeable interaction network models and explore their basic stati stical properties. These models allow for sparsity and power law degree di stributions\, both of which are widely observed empirical network properti es. I will start by presenting the Hollywood model\, which is computationa lly tractable\, admits a clear interpretation\, exhibits good theoretical properties\, and performs reasonably well in estimation and prediction.

\nIn many settings\, the series of interactions are structured. E-mail exchanges\, for example\, have a single sender and potentially multiple r eceivers. I will introduce hierarchical exchangeable interaction models fo r the study of structured interaction networks. In particular\, I will int roduce the Enron model as a canonical example\, which partially pools info rmation via a latent\, shared population-level distribution. A detailed si mulation study and supporting theoretical analysis provide clear model int erpretation\, and establish global power-law degree distributions. A compu tationally tractable Gibbs sampling algorithm is derived. Inference will b e shown on the Enron e-mail dataset. I will end with a discussion of how to perform posterior predictive checks on interaction data. Using these pr oposed checks\, I will show that the model fits the data well.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-10888@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Computational Anatomy: Structuring and Searching Shape S paces.\nAbstract: 100 years after the celebrated D’Arcy Thompson’s master piece “Growth and Forms”\, the modeling and the understanding of both vari ability and dynamics of related biological shapes are still particularly c hallenging from both modeling and computational point of view. The luminou s idea of his “Theory of Transformations” has been turned within the digit al era into a versatile mathematical and computational framework coined a s diffeomorphometry and living in the vicinity of riemannian geometry\, fl uid dynamics\, optimal control and statistics. We will discuss about the m athematical side of this framework as well as some of challenges that stil l need to be faced. DTSTART;TZID=America/New_York:20180503T133000 DTEND;TZID=America/New_York:20180503T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Alain Trouve’ (Ecole Normale Supe’rieure) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-alain-trouve-ecole-n ormale-superieure-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Computational Anatomy: Structuring and Searching Shape Spaces.<
/p>\n

**Abstract: **100 years after the celebrated D’Arcy
Thompson’s masterpiece “Growth and Forms”\, the modeling and the understan
ding of both variability and dynamics of related biological shapes are sti
ll particularly challenging from both modeling and computational point of
view. The luminous idea of his “Theory of Transformations” has been turned
within the digital era into a versatile mathematical and computational fr
amework coined as diffeomorphometry and living in the vicinity of riemann
ian geometry\, fluid dynamics\, optimal control and statistics. We will di
scuss about the mathematical side of this framework as well as some of cha
llenges that still need to be faced.

**Title
:** Growing Graceful Trees

**Abstract :**

In my talk I will describe and motivate the Graceful Labeling Conject ure. I will also describe constructions based on Gaussian elimination for listing and enumerating special induced edge label sequences of graphs. Ou r enumeration construction settles in the affirmative a conjecture raised by Whitty on the existence of matrix constructions whose determinant enume rate gracefully labeled trees. I will also describe and algorithm for obta ining all graceful labelings of a given graphs. If time permits I will con clude the paper with a conjugation algorithm which determines the set of g raphs on n vertices having no isolated vertices which admit no graceful la beling.

\nThe talk is based on joint work with Isaac Wass.

\n\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11696@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Hello\,\nThe Fall 2018 AMS Picnic will be held on Friday\, Sept ember 14th from 12-2pm in Great Hall which is located in the Levering Buil ding.\nSee you there. DTSTART;TZID=America/New_York:20180914T120000 DTEND;TZID=America/New_York:20180914T140000 SEQUENCE:0 SUMMARY:Fall 2018 AMS Department Picnic- Great Hall in Levering at 12pm URL:https://engineering.jhu.edu/ams/events/fall-2018-ams-department-picnic- great-hall-in-levering-at-12pm/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

Hello\,

\nThe Fall 2018 AMS Picnic will be held on Friday\, September 14^{th from 12-2pm in Great Hall which is located in the Levering Building.<
/p>\n}

See you there.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11731@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Learning Enabled Optimization\nAbstract: Traditionally \, Stochastic Optimization deals with optimization models in which some of the data is modeled using random variables. In contrast\, Learning Models are intended to capture the behavior of covariates\, where the goal is to characterize the behavior of the response (random variable) to the predic tors (random variables). The field of Statistical (or Machine) Learning f ocuses on understanding these relationships. The goal of this talk is to present a new class of composite optimization models in which the learning and optimization models live symbiotically. We will discuss several exam ples of such problems\, and how they give rise to a rich class of problems . (This talk is based on the work of several Ph.D. students\, and in part icular Yunxiao Deng\, Junyi Liu and Shuotao Diao).\nBio: Suvrajeet Sen is Professor at the Daniel J. Epstein Department of Industrial and Systems En gineering at the University of Southern California. Prior to joining USC\ , he was a Professor at Ohio State University and University of Arizona. H e has also served as the Program Director of OR as well as Service Enterpr ise Systems at the National Science Foundation. Professor Sen’s research is devoted to many categories of optimization models\, and he has publishe d over a hundred papers\, with the vast majority of them dealing with mode ls\, algorithms and applications of Stochastic Programming problems. He h as served on several editorial boards\, including Operations Research as A rea Editor for Optimization and as Associate Editor for INFORMS Journal on Computing\, Journal of Telecommunications Systems\, Mathematical Programm ing B\, and Operations Research. He also serves as an Advisory Editor fo r several newer journals and an Associate Editor of INFORMS J. on Optimiza tion. Professor Sen was instrumental in founding the INFORMS Optimization Society in 1995\, and has also served as its Chair (2015-16). Except for his years at NSF\, he has received continuous extramural research funding from NSF and other basic research agencies\, totaling over ten million do llars as PI over the past 25 years. In 2015\, this research and his group ’s contributions were recognized by the INFORMS Computing Society for semi nal work on Stochastic Mixed-Integer Programming. Professor Sen is a Fell ow of INFORMS. DTSTART;TZID=America/New_York:20180920T133000 DTEND;TZID=America/New_York:20180920T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Suvrajeet Sen (University of South California) @ White head 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-suvrajeet-sen-univer sity-of-south-california-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Learning Enabled Optimization

**Abstract: Traditionally\, Stochastic Optimization deals with optimization models
in which some of the data is modeled using random variables. In contrast\
, Learning Models are intended to capture the behavior of covariates\, whe
re the goal is to characterize the behavior of the response (random variab
le) to the predictors (random variables). The field of Statistical (or Ma
chine) Learning focuses on understanding these relationships. The goal of
this talk is to present a new class of composite optimization models in w
hich the learning and optimization models live symbiotically. We will dis
cuss several examples of such problems\, and how they give rise to a rich
class of problems. (This talk is based on the work of several Ph.D. stude
nts\, and in particular Yunxiao Deng\, Junyi Liu and Shuotao Diao).**

**Title
: **Incorporating Confidence into Systemic Risk

**Abs
tract**: In a crisis when faced with insolvency\, banks can issue s
hares/sell their treasury stock in the stock market and borrow money in or
der to raise funds. We propose a simple model to find the maximum amount o
f new funds the banks can raise in this way. To do this we incorporate mar
ket confidence of the bank together with market confidence of all the othe
r banks into the overnight borrowing rate.

Additionally\, for a gi ven shortfall\, we find the optimal mix of borrowing and stock selling. We show that the existence and uniqueness of Nash equilibrium strategy for a ll these problems. We then calibrate this model to market data and conduct an empirical study to access whether the current financial system is safe r than it was before the last financial crisis.

\nIn a related model of financial contagion in a network subject to fire sales and price impac ts\, we allow for firms to borrow to cover their shortfall as well. We con sider both uncollateralized and collateralized loans. The main results of this work are providing sufficient conditions for existence and uniqueness of the clearing solutions (i.e.\, payments\, liquidations\, and borrowing )\; in such a setting any clearing solution is the Nash equilibrium of an aggregation game.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-12409@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Consistent Inter-Model Specification for Stochastic Volatility and VIX Market Models\nAbstract: This talk addresses the follow ing question: If a stochastic model is specified for the curve of VIX futu res\, what are the restrictions in order for it to be consistent with a st ochastic volatility model? In other words\, assuming that a stochastic vol atility model is in place\, a so-called market model will need to satisfy some conditions in order for there to not be any inter-model arbitrage or mis-priced derivatives. The present work gives such a condition\, and also shows how to recover the correctly specified stochastic volatility functi on from the market model. DTSTART;TZID=America/New_York:20181002T133000 DTEND;TZID=America/New_York:20181002T143000 SEQUENCE:0 SUMMARY:FM Seminar: Andrew Papanicolaou (NYU) @ Levering Arellano URL:https://engineering.jhu.edu/ams/events/fm-seminar-andrew-papanicolaou-n yu-levering-arellano/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: ** Consistent Inter-Model Specification for Stochastic Volatil
ity and VIX Market Models

**Abstract: **This talk add
resses the following question: If a stochastic model is specified for the
curve of VIX futures\, what are the restrictions in order for it to be con
sistent with a stochastic volatility model? In other words\, assuming that
a stochastic volatility model is in place\, a so-called market model will
need to satisfy some conditions in order for there to not be any inter-mo
del arbitrage or mis-priced derivatives. The present work gives such a con
dition\, and also shows how to recover the correctly specified stochastic
volatility function from the market model.

**Title
: **Multivariate Records

**Abstract**: Given a
vector-valued time series\, a multivariate record is said to occur at som
e time if no previous observation dominates it in every coordinate. This n
otion of a record generalizes the usual notion in one dimension\, and give
s rise to some interesting phenomena\, some of which will be presented. An
efficient algorithm for sampling the multivariate records process that en
ables one to study the process empirically and discover new phenomena rela
ted to record growth in time will be described\, and theoretical results i
lluminated from simulations will be presented. (This is joint work with Fr
ed Torcaso and Vincent Lyzinzki).

**Title
:** Hilbert’s Nullstellensatz and Linear Algebra: An Algorithm for
Determining Combinatorial Infeasibility

**Abstract:**

Unlike systems of linear equations\, systems of multivariate poly nomial equations over the complex numbers or finite fields can be compactl y used to model combinatorial problems. In this way\, a problem is feasibl e (e.g. a graph is 3-colorable\, Hamiltonian\, etc.) if and only if a give n system of polynomial equations has a solution. Via Hilbert’s Nullstellen satz\, we generate a sequence of large-scale\, sparse linear algebra compu tations from these non-linear models to describe an algorithm for solving the underlying combinatorial problem. As a byproduct of this algorithm\, w e produce algebraic certificates of the non-existence of a solution (i.e.\ , non-3-colorability\, non-Hamiltonicity\, or non-existence of an independ ent set of size k).

\nIn this talk\, we present theoretical and expe rimental results on the size of these sequences\, and the complexity of th e Hilbert’s Nullstellensatz algebraic certificates. For non-3-colorability over a finite field\, we utilize this method to successfully solve graph problem instances having thousands of nodes and tens of thousands of edges . We also describe methods of optimizing this method\, such as finding alt ernative forms of the Nullstellensatz\, adding carefully-constructed polyn omials to the system\, branching and exploiting symmetry.

\nGraduate students are happily advised that no background in algebraic geometry or familiarity with Hilbert’s Nullstellensatz is assumed for this talk. All t heorems and terms are clearly explained with friendly pictures and example s. 🙂

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11741@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Generative Models to Decode Brain Pathology\nAbstract:\n Clinical neuroscience is field with all the difficulties that come from hi gh dimensional data\, and none of the advantages that fuel modern-day brea kthroughs in computer vision\, automated speech recognition\, and health i nformatics. It is a field of unavoidably small datasets\, due to the costl y acquisitions and environmental confounds\, massive patient variability\, and an arguable lack of ground truth information. My lab tackles these ch allenges by combining analytical tools from signal processing and machine learning with hypothesis-driven insights about the brain.\nThis talk will highlight three ongoing projects in my lab that span a range of methodolog ies and clinical applications. First\, I will develop a joint optimization framework to predict clinical severity from resting-state fMRI data. Our model is based on two coupled terms: a generative non-negative matrix fact orization and a discriminative linear regression. This project is part of our larger effort to better characterize heterogeneous patient cohorts. Ne xt\, I will describe a spatio-temporal model to track the spread of epilep tic seizures from EEG data. Unlike conventional approaches\, our model rel ies on a latent network structure that captures the hidden state of each E EG channel\; the latent variables are complemented by an intuitive likelih ood model for the observed neuroimaging measures. This project takes the f irst steps toward noninvasive seizure localization. Finally\, I will highl ight a very recent initiative in my lab to manipulate emotional cues in hu man speech. Our long-term goal is to create a naturalistic therapy for aut ism.\nBiography:\nArchana Venkataraman is a John C. Malone Assistant Profe ssor in the Department of Electrical and Computer Engineering at Johns Hop kins University. She directs the Neural Systems Analysis Laboratory and is a core faculty member of the Malone Center for Engineering in Healthcare. Dr. Venkataraman’s research lies at the intersection of multimodal integr ation\, network modeling and clinical neuroscience. Her work has yielded n ovel insights in to debilitating neurological disorders\, such as autism\, schizophrenia and epilepsy\, with the long-term goal of improving patient care. Dr. Venkataraman completed her B.S.\, M.Eng. and Ph.D. in Electrica l Engineering at MIT in 2006\, 2007 and 2012\, respectively. She is a reci pient of the CHDI Grant on network models for Huntington’s Disease\, the M IT Lincoln Lab campus collaboration award\, the NIH Advanced Multimodal Ne uroimaging Training Grant\, the National Defense Science and Engineering G raduate Fellowship\, the Siebel Scholarship and the MIT Provost Presidenti al Fellowship. DTSTART;TZID=America/New_York:20181018T133000 DTEND;TZID=America/New_York:20181018T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Archana Venkataraman (Electrical & Computer Engineerin g) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-archana-venkataraman -electrical-computer-engineering-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Generative Models to Decode Brain Pathology

**Abs
tract:**

Clinical neuroscience is field with all the diffic ulties that come from high dimensional data\, and none of the advantages t hat fuel modern-day breakthroughs in computer vision\, automated speech re cognition\, and health informatics. It is a field of unavoidably small dat asets\, due to the costly acquisitions and environmental confounds\, massi ve patient variability\, and an arguable lack of ground truth information. My lab tackles these challenges by combining analytical tools from signal processing and machine learning with hypothesis-driven insights about the brain.

\nThis talk will highlight three ongoing projects in my lab that span a range of methodologies and clinical applications. First\, I wi ll develop a joint optimization framework to predict clinical severity fro m resting-state fMRI data. Our model is based on two coupled terms: a gene rative non-negative matrix factorization and a discriminative linear regre ssion. This project is part of our larger effort to better characterize he terogeneous patient cohorts. Next\, I will describe a spatio-temporal mode l to track the spread of epileptic seizures from EEG data. Unlike conventi onal approaches\, our model relies on a latent network structure that capt ures the hidden state of each EEG channel\; the latent variables are compl emented by an intuitive likelihood model for the observed neuroimaging mea sures. This project takes the first steps toward noninvasive seizure local ization. Finally\, I will highlight a very recent initiative in my lab to manipulate emotional cues in human speech. Our long-term goal is to create a naturalistic therapy for autism.

\n**Biography:**

Archana Venkataraman is a John C. Malone Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins Univers ity. She directs the Neural Systems Analysis Laboratory and is a core facu lty member of the Malone Center for Engineering in Healthcare. Dr. Venkata raman’s research lies at the intersection of multimodal integration\, netw ork modeling and clinical neuroscience. Her work has yielded novel insight s in to debilitating neurological disorders\, such as autism\, schizophren ia and epilepsy\, with the long-term goal of improving patient care. Dr. V enkataraman completed her B.S.\, M.Eng. and Ph.D. in Electrical Engineerin g at MIT in 2006\, 2007 and 2012\, respectively. She is a recipient of the CHDI Grant on network models for Huntington’s Disease\, the MIT Lincoln L ab campus collaboration award\, the NIH Advanced Multimodal Neuroimaging T raining Grant\, the National Defense Science and Engineering Graduate Fell owship\, the Siebel Scholarship and the MIT Provost Presidential Fellowshi p.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11745@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Subset selection in sparse matrices\nAbstract: In subset selection\, we search for the best linear predictor that involves a small subset of variables. From a computational complexity viewpoint\, subset s election is NP-hard and few classes are known to be solvable in polynomial time. Using mainly tools from discrete geometry\, we show that some spars ity conditions on the original data matrix allow us to solve the problem i n polynomial time.\nThis is joint work with Alberto Del Pia and Robert Wei smantel DTSTART;TZID=America/New_York:20181025T133000 DTEND;TZID=America/New_York:20181025T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Santanu Dey (Georgia Tech) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-santanu-dey-gatech-w hitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Subset selection in sparse matrices

**Abstract: In subset selection\, we search for the best linear predictor that
involves a small subset of variables. From a computational complexity vie
wpoint\, subset selection is NP-hard and few classes are known to be solva
ble in polynomial time. Using mainly tools from discrete geometry\, we sho
w that some sparsity conditions on the original data matrix allow us to so
lve the problem in polynomial time.**

This is joint work with Albert o Del Pia and Robert Weismantel

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11746@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: An Introduction to Randomized Algorithms\nfor Matrix Com putations\nAbstract: The emergence of massive data sets\, over the past tw enty or so years\, has lead to the development of Randomized Numerical Li near Algebra.\nFast and accurate randomized matrix algorithms are being de signed for\napplications like machine learning\, population genomics\, ast ronomy\, nuclear engineering\, and optimal experimental design.\nWe give a flavour of randomized algorithms for the solution of least\nsquares/regre ssion problems. Along the way we illustrate important\nconcepts from numer ical analysis (conditioning and pre-conditioning)\,\nprobability (concentr ation inequalities)\, and statistics (sampling and leverage scores). DTSTART;TZID=America/New_York:20181101T133000 DTEND;TZID=America/New_York:20181101T143000 SEQUENCE:0 SUMMARY:AMS Seminar: IIse Ipsen (North Carolina State University) @ Whitehe ad 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-iise-ipsen-north-car olina-state-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: **An Introduction to Randomized Algorithms

\nfor Matrix Comp
utations

**Abstract**: The emergence of massive data
sets\, over the past twenty or so years\, has lead to the development of
Randomized Numerical Linear Algebra.

\nFast and accurate randomized m
atrix algorithms are being designed for

\napplications like machine l
earning\, population genomics\, astronomy\, nuclear engineering\, and opti
mal experimental design.

We give a flavour of randomized algorithm
s for the solution of least

\nsquares/regression problems. Along the
way we illustrate important

\nconcepts from numerical analysis (condi
tioning and pre-conditioning)\,

\nprobability (concentration inequali
ties)\, and statistics (sampling and leverage scores).

**Title
:** Determining the number of communities in degree-corrected stoch
astic block models.

**Abstract: **We propose to estim
ate the number of communities in degree-corrected stochastic block models
based on a pseudo likelihood ratio. For estimation\, we consider a spectra
l clustering together with binary segmentation method. This approach guara
ntees an upper bound for the pseudo likelihood ratio statistic when the mo
del is over-fitted. We also derive its limiting distribution when the mode
l is under-fitted. Based on these properties\, we establish the consistenc
y of our estimator for the true number of communities. Developing these th
eoretical properties require a mild condition on the average degree — grow
ing at a rate faster than log(n)\, where n is the number of nodes. Our pro
posed method is further illustrated by simulation studies and analysis of
real-world networks. The numerical results show that our approach has sati
sfactory performance when the network is sparse and/or has unbalanced comm
unities.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11756@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: A limit theorem for an omnibus embedding of multiple ran dom graphs\nAbstract: Performing statistical inference on collections of g raphs is of import to many disciplines. Graph embedding\, in which the ver tices of a graph are mapped to vectors in a low-dimensional Euclidean spac e\, has gained traction as a basic tool for graph analysis. We describe an omnibus embedding in which multiple graphs on the same vertex set are joi ntly embedded into a single space with a distinct representation for each graph. We prove a central limit theorem for this omnibus embedding\, and s how that this simultaneous embedding into a common space allows comparison of graphs without the need to perform pairwise alignments of graph embedd ings. Experimental results demonstrate that the omnibus embedding improves upon existing methods\, and in particular provides insight into analysis of real connectomic data.\n DTSTART;TZID=America/New_York:20181115T133000 DTEND;TZID=America/New_York:20181115T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Avanti Athreya (AMS) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-madeleine-udell-corn ell-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

** Title:** A limit theorem for an omnibus embedding of multiple ra
ndom graphs

**Abstract:** Performing s
tatistical inference on collections of graphs is of import to many disciplines. Graph embedding\, in which the vertices
of a graph are mapped to vectors in
a low-dimensional Euclidean space\, has gained traction as a basic tool for graph analysis. We describe an o
mnibus embedding in which multiple
graphs on the same vertex set are jointly embedded into a single ~~space with a distinct representation for eac
h graph. We prove a central limit ~~~~t
heorem for this omnibus embedding\, and show that this simultaneous embedding into a common space allows comp
arison of graphs without the need to perform pairwise alignments of graph embeddings. Experimental results demonstrate that the omnibus embeddin
g improves upon existing methods\, and in particular provides insight into
analysis of real connectomic data.~~

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11757@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: How nature might endow cortical regions of the mammalian brain with diffeomorphisms?\nAbstract: A surface-based diffeomorphic algo rithm to generate 3D coordinate grids in the cortical ribbon in the mammal ian brain is described. In the grid\, normal coordinate lines are generate d by the diffeomorphic evolution from the grey/white (inner) surface to th e grey/csf (outer) surface. Here\, the cortical ribbon is described by two triangulated surfaces with open boundaries. It is assumed that the cortic al ribbon consists of cortical columns which are orthogonal to the white m atter surface. This might be viewed as a consequence of the embryonic deve lopment of the columns. It is also assumed that the columns are orthogonal to the outer surface so that the resultant vector field is orthogonal to the evolving surface. The laminar properties of the cortical ribbon\, i.e. cortical layers\, are then characterized by the normal lines. The distanc e of the normal lines from the vector field such that the inner surface ev olves diffeomorphically towards the outer one can be construed as a measur e of thickness. Finally\, an equivolumetric reparametrization of the diffe omorphism is developed to ensure volumetric preservation of cortical layer s across highly folded regions such as gyri and sulci. Applications are de scribed for human and feline brains.\n DTSTART;TZID=America/New_York:20181129T133000 DTEND;TZID=America/New_York:20181129T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Tilak Ratnanather (BME) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-tba-whitehead-304-7/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
:** How nature might endow cortical regions of the mammalian brain
with diffeomorphisms?

**Abstract**: A surface-based d
iffeomorphic algorithm to generate 3D coordinate grids in the cortical rib
bon in the mammalian brain is described. In the grid\, normal coordinate l
ines are generated by the diffeomorphic evolution from the grey/white (inn
er) surface to the grey/csf (outer) surface. Here\, the cortical ribbon is
described by two triangulated surfaces with open boundaries. It is assume
d that the cortical ribbon consists of cortical columns which are orthogon
al to the white matter surface. This might be viewed as a consequence of t
he embryonic development of the columns. It is also assumed that the colum
ns are orthogonal to the outer surface so that the resultant vector field
is orthogonal to the evolving surface. The laminar properties of the corti
cal ribbon\, i.e. cortical layers\, are then characterized by the normal l
ines. The distance of the normal lines from the vector field such that the
inner surface evolves diffeomorphically towards the outer one can be cons
trued as a measure of thickness. Finally\, an equivolumetric reparametriza
tion of the diffeomorphism is developed to ensure volumetric preservation
of cortical layers across highly folded regions such as gyri and sulci. Ap
plications are described for human and feline brains.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-11761@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Investigating Spatially Complex Data with Topological D ata Analysis\n \nAbstract: Data exhibiting complicated spatial structures are common in many areas of science (e.g. cosmology\, biology)\, but can b e difficult to analyze. Persistent homology is a popular approach within t he area of Topological Data Analysis (TDA) that offers a way to represent\ , visualize\, and interpret complex data by extracting topological feature s\, which can be used to infer properties of the underlying structures. Fo r example\, TDA may be useful for analyzing the large-scale structure (LSS ) of the Universe\, which is an intricate and spatially complex web of mat ter. The output from persistent homology\, called persistence diagrams\, s ummarize the different ordered holes in the data (e.g. connected component s\, loops\, voids). I will introduce persistent homology\, present functio nal transformations of persistence diagrams useful for inference\, and dis cuss several applications. DTSTART;TZID=America/New_York:20181206T133000 DTEND;TZID=America/New_York:20181206T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Jessi Cisewski (Yale University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-jessi-cisewski-yale- university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
:** Investigati
ng Spatially Complex Data with Topological Data Analysis

< /p>\n

**Abstract**: Data exhibiting complicated spatial str
uctures are common in many areas of science (e.g. cosmology\, biology)\, b
ut can be difficult to analyze. Persistent homology is a popular approach
within the area of Topological Data Analysis (TDA) that offers a way to re
present\, visualize\, and interpret complex data by extracting topological
features\, which can be used to infer properties of the underlying struct
ures. For example\, TDA may be useful for analyzing the large-scale struct
ure (LSS) of the Universe\, which is an intricate and spatially complex we
b of matter. The output from persistent homology\, called persistence diag
rams\, summarize the different ordered holes in the data (e.g. connected c
omponents\, loops\, voids). I will introduce persistent homology\, present
functional transformations of persistence diagrams useful for inference\,
and discuss several applications.

**Title
: **Shape Spaces of Curves

**Abstract: **The
talk will discuss results\, old and new\, on a class of metrics on length
-normalized curves in d dimensions\, represented by their unit tangents e
xpressed as a function of arc-length considered as functions from the unit
interval to the d-dimensional unit sphere. These metrics are derived from
the combined action of diffeomorphisms (change of parameters) and arc-len
gth-dependent rotation acting on the tangent. Minimizing a Riemannian metr
ic balancing a right-invariant metric on diffeomorphisms and an L2 norm on
the motion of tangents leads to a special case of “metamorphosis\,” for w
hich the computation of geodesic distances can be dramatically simplified
after a suitable transformation of the curves into elements of a Hilbert s
phere.

**Title
: **Complexity in Simple Cross-Sectional Data with Binary Disease O
utcome

**Abstract: **Cross-sectionally sampled data
with binary disease outcome are commonly collected and analyzed in observa
tional studies for understanding how covariates correlate with disease oc
currence. At Hopkins SPH and SOM\, cross-sectional data analyses are also
commonly included in master and doctoral dissertations. This talk will ad
dress two questions: (1) Which risk can be identified in a commonly adopt
ed model (such as the logistic model)? (2) Are there problems when inter
preting the identifiable risk? As the progression of a disease typically i
nvolves both disease status and duration\, this talk considers how the bi
nary disease outcome is connected to the progression of disease through th
e birth-illness-death process. In general\, we conclude that the distribu
tion of cross-sectional binary outcome could be very different from the
population risk distribution. The cross-sectional risk probability is det
ermined jointly by the population risk probability together with the rati
o of duration of diseased state to the duration of disease-free state. Usi
ng the logistic model as an illustrating example\, we examine the bias f
rom cross-sectional data and argue that the bias can almost never be avoi
ded. We present an approach which treats the binary outcome as a specific
type of current status data and offers a compromised model on the basis of
an age-specific risk probability (ARP)\, though the interpretation of th
e ARP itself could also be questioned. An analysis based on Alzheimer’s d
isease data is presented to illustrate the ARP approach and data complexit
y. (This is joint work with Yuchen Yang\, Department of Biostatistics\, Jo
hns Hopkins University).

\n

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-12878@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Towards Robust and Scalable Private Data Analysis\nAbstr act:\nIn the current age of big data\, we are constantly creating new data which is analyzed by various platforms to improve service and user’s expe rience. Given the sensitive and confidential nature of these data\, there are obvious security and privacy concerns while storing and analyzing suc h data. In this talk\, I will discuss the fundamental challenges in provid ing robust security and privacy guarantee while storing and analyzing larg e data. I will also give a brief overview of my contributions and future p lans towards addressing these challenges.\nTo give a glimpse of these chal lenges in providing a robust privacy guarantee known as differential priva cy\, I will use spectral sparsification of graphs as an example. Given the ubiquitous nature of graphs\, differentially private analysis on graphs h as gained a lot of interest. However\, existing algorithms for these analy ses are tailored made for the task at hand making them infeasible in pract ice. In this talk\, I will present a novel differentially private algorith m that outputs a spectral sparsification of the input graph. At the core o f this algorithm is a method to privately estimate the importance of an ed ge in the graph. Prior to this work\, there was no known privacy preservin g method that provides such an estimate or spectral sparsification of grap hs.\nSince many graph properties are defined by the spectrum of the graph\ , this work has many analytical as well as learning theoretic applications . To demonstrate some applications\, I will show more efficient and accura te analysis of various combinatorial problems on graphs and the first tech nique to perform privacy preserving manifold learning on graphs. DTSTART;TZID=America/New_York:20190211T100000 DTEND;TZID=America/New_York:20190211T110000 SEQUENCE:0 SUMMARY:Mathematics Seminar- Jalaj Upadhyay (Computer Science): Optimizatio n and Discrete @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/mathematics-seminar-optimization -and-discrete-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
:** Towards Robust and Scalable Private Data Analysis

In the current age of big data\, we are cons tantly creating new data which is analyzed by various platforms to improve service and user’s experience. Given the sensitive and confidential natu re of these data\, there are obvious security and privacy concerns while s toring and analyzing such data. In this talk\, I will discuss the fundamen tal challenges in providing robust security and privacy guarantee while st oring and analyzing large data. I will also give a brief overview of my co ntributions and future plans towards addressing these challenges.

\nTo give a glimpse of these challenges in providing a robust privacy guaran tee known as differential privacy\, I will use spectral sparsification of graphs as an example. Given the ubiquitous nature of graphs\, differential ly private analysis on graphs has gained a lot of interest. However\, exis ting algorithms for these analyses are tailored made for the task at hand making them infeasible in practice. In this talk\, I will present a novel differentially private algorithm that outputs a spectral sparsification of the input graph. At the core of this algorithm is a method to privately e stimate the importance of an edge in the graph. Prior to this work\, there was no known privacy preserving method that provides such an estimate or spectral sparsification of graphs.

\nSince many graph properties are defined by the spectrum of the graph\, this work has many analytical as w ell as learning theoretic applications. To demonstrate some applications\, I will show more efficient and accurate analysis of various combinatorial problems on graphs and the first technique to perform privacy preserving manifold learning on graphs.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-12771@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Optimization and Topology: Two Stories\nAbstract: Algebr aic topology and optimization are typically not considered as closely rela ted fields of mathematics. We will present two stories of fruitful interac tion between these two fields\, with the implications going the opposite w ay in the two cases.\nIn the first result\, we consider the question of th e existence of certain nice decompositions of generalized surfaces called currents in geometric measure theory. In the finite setting\, we could use tools from algebraic topology to pose this question as that of the existe nce of integer solutions to a certain linear programming (LP) problem. Fol lowing classical results on LP that rely on total unimodularity (TU) of ma trices\, the answer is known in codimension 1. We develop tools to push th is result to the infinite case\, showing that under certain assumptions th e TU result from LP implies the existence result for codimension 1 current s in general.\nIn the second story\, we consider new approaches to charact erize the robustness of solutions to a system of nonlinear equations. This problem arises in many applications such as the power grid and other infr astructure networks. We use techniques from algebraic topology (topologica l degree theory) to characterize the robustness margin of such systems of equations. We then cast the problem of checking for the specified conditio ns as a nonlinear optimization problem. Based on this formulation\, we dev elop efficient computational techniques to estimate lower and upper bounds for the robustness margin.\n \n DTSTART;TZID=America/New_York:20190214T133000 DTEND;TZID=America/New_York:20190214T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Bala Krishnamorthy (Washington State University) @ Whi tehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-bala-krishnamorthy-w ashington-state-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
:** Optimization and Topology: Two Stories

**Abstract
: **Algebraic topology and optimization are typically not considere
d as closely related fields of mathematics. We will present two stories of
fruitful interaction between these two fields\, with the implications goi
ng the opposite way in the two cases.

In the first result\, we con sider the question of the existence of certain nice decompositions of gene ralized surfaces called currents in geometric measure theory. In the finit e setting\, we could use tools from algebraic topology to pose this questi on as that of the existence of integer solutions to a certain linear progr amming (LP) problem. Following classical results on LP that rely on total unimodularity (TU) of matrices\, the answer is known in codimension 1. We develop tools to push this result to the infinite case\, showing that unde r certain assumptions the TU result from LP implies the existence result f or codimension 1 currents in general.

\nIn the second story\, we con sider new approaches to characterize the robustness of solutions to a syst em of nonlinear equations. This problem arises in many applications such a s the power grid and other infrastructure networks. We use techniques from algebraic topology (topological degree theory) to characterize the robust ness margin of such systems of equations. We then cast the problem of chec king for the specified conditions as a nonlinear optimization problem. Bas ed on this formulation\, we develop efficient computational techniques to estimate lower and upper bounds for the robustness margin.

\n\n

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-12776@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Mortgage Credit\, Aggregate Demand\, and Unconventional Monetary Policy\nAbstract: I develop a quantitative model of the mortgage market operating in an economy with financial frictions and nominal rigidi ties. I use this model to study the effectiveness of large-scale asset pur chases (LSAPs) by a central bank as a tool of monetary policy. When negati ve shocks hit\, homeowner and financial sector balance sheets are impaired \, borrowing constraints bind\, asset prices and aggregate demand drop\, h ampering the transmission of conventional monetary policy. LSAPs boost agg regate demand in a crisis by directing additional lending to homeowners\, raising house prices\, and stablishing expectations of future financial st ability. However\, legacy household debt depresses output and consumption in recovery. In the long run\, a commitment to ongoing use of LSAPs in cri ses reduces credit and business cycle volatility and redistributes resourc es from borrowers and intermediaries to savers. DTSTART;TZID=America/New_York:20190221T133000 DTEND;TZID=America/New_York:20190221T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Vadim Elenev (JHU-Carey Business School) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-vadim-elenev-jhu-bus iness-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
:** Mortgage Credit\, Aggregate Demand\, and Unconventional Monetar
y Policy

**Abstract: **I develop a quantitative model
of the mortgage market operating in an economy with financial frictions a
nd nominal rigidities. I use this model to study the effectiveness of larg
e-scale asset purchases (LSAPs) by a central bank as a tool of monetary po
licy. When negative shocks hit\, homeowner and financial sector balance sh
eets are impaired\, borrowing constraints bind\, asset prices and aggregat
e demand drop\, hampering the transmission of conventional monetary policy
. LSAPs boost aggregate demand in a crisis by directing additional lending
to homeowners\, raising house prices\, and stablishing expectations of fu
ture financial stability. However\, legacy household debt depresses output
and consumption in recovery. In the long run\, a commitment to ongoing us
e of LSAPs in crises reduces credit and business cycle volatility and redi
stributes resources from borrowers and intermediaries to savers.

**Title
:** Big Data is Low Rank

**Abstract: **Matri
ces of low rank are pervasive in big data\, appearing in recommender syste
ms\, movie preferences\, topic models\, medical records\, and genomics.

While there is a vast literature on how to exploit low rank structur e in these datasets\, there is less attention on explaining why low rank s tructure appears in the first place.

\nIn this talk\, we explain the abundance of low rank matrices in big data by proving that certain latent variable models associated to piecewise analytic functions are of log-ran k. Any large matrix from such a latent variable model can be approximated\ , up to a small error\, by a low rank matrix.

\nArmed with this theo rem\, we show how to use a low rank modeling framework to exploit low rank structure even for datasets that are not numeric\, with applications in t he social sciences\, medicine\, retail\, and machine learning.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-12781@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Market Microstructure Invariance: A Dynamic Equilibrium Model\nAbstract: Invariance relationships are derived in a dynamic\, infin ite-horizon\, equilibrium model of adverse selection with risk-neutral inf ormed traders\, noise traders\, risk-neutral market makers\, and endogenou s information production. Scaling laws for bet size and transaction costs require the assumption that the effort required to generate one bet does n ot vary across securities and time. Scaling laws for pricing accuracy and market resiliency require the additional assumption that private informati on has the same signal-to-noise ratio across markets. Prices follow a mart ingale with endogenously derived stochastic volatility. Returns volatility \, pricing accuracy\, market depth\, and market resiliency are closely rel ated to one another. The model solution depends on two state variables: st ock price and hard-to- observe pricing accuracy. Invariance makes predicti ons operational by expressing them in terms of log-linear functions of eas ily observable variables such as price\, volume\, and volatility.\n DTSTART;TZID=America/New_York:20190307T133000 DTEND;TZID=America/New_York:20190307T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Albert “Pete” Kyle (University of MD) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-albert-pete-kyle-uni versity-of-md-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Title
: **Market Microstructure Invariance: A Dynamic Equilibrium Model\n

**Abstract: **Invariance relationships are derived in
a dynamic\, infinite-horizon\, equilibrium model of adverse selection with
risk-neutral informed traders\, noise traders\, risk-neutral market maker
s\, and endogenous information production. Scaling laws for bet size and t
ransaction costs require the assumption that the effort required to genera
te one bet does not vary across securities and time. Scaling laws for pric
ing accuracy and market resiliency require the additional assumption that
private information has the same signal-to-noise ratio across markets. Pri
ces follow a martingale with endogenously derived stochastic volatility. R
eturns volatility\, pricing accuracy\, market depth\, and market resilienc
y are closely related to one another. The model solution depends on two st
ate variables: stock price and hard-to- observe pricing accuracy. Invarian
ce makes predictions operational by expressing them in terms of log-linear
functions of easily observable variables such as price\, volume\, and vol
atility.

\n END:VEVENT BEGIN:VEVENT UID:ai1ec-12786@engineering.jhu.edu/ams DTSTAMP:20190715T220523Z CATEGORIES: CONTACT: DESCRIPTION:Title: Uncertainty Quantification and Nonparametric Inference f or Complex Data and Simulations\nAbstract: Recent technological advances have led to a rapid growth in not just the amount of scientific data but a lso their complexity and richness. Simulation models have\, at the same ti me\, become increasingly detailed and better at capturing the underlying p rocesses that generate observable data. On the statistical methods front\, however\, we still lack tools that accurately quantify complex relationsh ips between data and model parameters\, as well as adequate tools to valid ate models of multivariate likelihoods and posteriors. In this talk\, I wi ll discuss our current work on addressing some of the multi-faceted challe nges encountered in astronomy but more generally applicable to fields invo lving massive amounts of complex data and simulations\; in particularly\, challenges related to (i) building conditional probability models that can handle inputs of different modalities\, e.g. photometric data and correla tion functions\, (ii) estimating non-Gaussian likelihoods and posteriors v ia simulations\, and (iii) assessing the performance of complex models and simulations when the true distributions are not known. I will draw exampl es from photometric redshift estimation and from the inference of cosmolog ical parameters. (Part of this work is joint with Rafael Izbicki\, Taylor Pospisil\, Peter Freeman\, Ilmun Kim\, and the LSST-DESC PZ working group) DTSTART;TZID=America/New_York:20190314T133000 DTEND;TZID=America/New_York:20190314T143000 SEQUENCE:0 SUMMARY:AMS Seminar: Ann Lee (Carnegie Mellon University) @ Whitehead 304 URL:https://engineering.jhu.edu/ams/events/ams-seminar-ann-lee-carnegie-mel lon-university-whitehead-304/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
: **Uncertainty Quantification and Nonparametric Inference for Comp
lex Data and Simulations

**Abstract: **Recent techno
logical advances have led to a rapid growth in not just the amount of scie
ntific data but also their complexity and richness. Simulation models have
\, at the same time\, become increasingly detailed and better at capturing
the underlying processes that generate observable data. On the statistica
l methods front\, however\, we still lack tools that accurately quantify c
omplex relationships between data and model parameters\, as well as adequa
te tools to validate models of multivariate likelihoods and posteriors. In
this talk\, I will discuss our current work on addressing some of the mul
ti-faceted challenges encountered in astronomy but more generally applicab
le to fields involving massive amounts of complex data and simulations\; i
n particularly\, challenges related to (i) building conditional probabilit
y models that can handle inputs of different modalities\, e.g. photometric
data and correlation functions\, (ii) estimating non-Gaussian likelihoods
and posteriors via simulations\, and (iii) assessing the performance of c
omplex models and simulations when the true distributions are not known. I
will draw examples from photometric redshift estimation and from the infe
rence of cosmological parameters. (Part of this work is joint with Rafael
Izbicki\, Taylor Pospisil\, Peter Freeman\, Ilmun Kim\, and the LSST-DESC
PZ working group)

**Title
:** Guiding clinical and preclinical investigations of breast cance
r with mathematical modeling and analyses

**Abstract**: One of the great challenges for cancer treatment is the inability to op
timize therapy. Without a reasonable mathematical framework\, our ability
to select treatment regimens for the individual patient is fundamentally l
imited to trial and error. Presented here are examples of data-driven\, in
tegrated experimental-mathematical approaches to studying breast cancer’s
response to therapy for both pre-clinical and clinical investigations. The
preclinical model\, consisting of ODEs\, connects various experiments for
an *in vivo* mouse system to better understand the interactions of
the immune response and targeted therapy for breast cancer. The clinical
model is a 3D PDE system for predicting tumor response to neoadjuvant ther
apy using patient-specific data that lays the groundwork for optimizing ch
emotherapeutic dosing and scheduling. In both examples\, the results of un
certainty and sensitivity analyses are discussed to show how they can be u
sed to generate experimentally testable hypotheses\, narrow the scope for
experimental investigations\, and evolve mathematical models. Additionally
\, multi-scale models are proposed that bridge the gap between *in vitr
o* and *in vivo* experiments to step towards clinical translati
on.

**Title: **Min-Ma
x Relations for Packing and Covering

**Abstract: **We
consider a family M of subsets of a finite set E. A “cover” is a subset o
f E that intersects every member of the family M. A “packing” is a set of
members of M no two of which intersect. Clearly\, the cardinality of a pac
king is at most that of a cover. We study conditions under which the maxim
um cardinality of a packing equals the minimum cardinality of a cover. We
present recent results obtained jointly with Ahmad Abdi and Dabeen Lee.

**Bio: ** Gerard Cornuejols is professor of Operations
Research at Carnegie Mellon University. His research interests are in inte
ger programming and combinatorial optimization. He received the Lanchester
Prize twice (1978 and 2015)\, the Fulkerson Prize (2000)\, the Dantzig Pr
ize (2009) and the von Neumann Theory Prize (2011).

**Title
: **Robust inference with the knockoff filter.

**Abst
ract: **In this talk\, I will present ongoing work on the knockoff
filter for inference in regression. In a high-dimensional model selection
problem\, we would like to select relevant features without too many false
positives. The knockoff filter provides a tool for model selection by cre
ating knockoff copies of each feature\, testing the model selection algori
thm for its ability to distinguish true from false covariates to control t
he false positives. In practice\, the modeling assumptions that underlie t
he construction of the knockoffs may be violated\, as we cannot know the e
xact dependence structure between the various features. Our ongoing work a
ims to determine and improve the robustness properties of the knockoff fra
mework in this setting. We find that when knockoff features are constructe
d using estimated feature distributions whose errors are small in a KL div
ergence type measure\, the knockoff filter provably controls the false dis
covery rate at only a slightly higher level. This work is joint with Emman
uel Candès and Richard Samworth.

This is joint work with Emmanuel Candès\, Aa ditya Ramdas\, and Ryan Tibshirani.

\n**Bio: **T
BA

**Title:**
Distribution free prediction: Is conditional inference possible?

This is joint work with Emmanuel Candès\, Aaditya Ramdas\ , and Ryan Tibshirani.

\n**Bio: **TBA

**Title
: **“Real-time” optimization under forwa
rd rank-dependent processes: time-consistent optimality under probability
distortions

**Abstract: **Forward performance processes are defined via time-consistent optim
ality and incorporate “real-time” incoming information. On the other hand\
, popular performance criteria – for example\, mean-variance optimization\
, hyperbolic discounting\, probability distortions – are by nature time-in
consistent. How to define forward performance criteria in time-inconsisten
t settings then becomes a challenging problem\, both conceptually and tech
nically. In this talk\, I will discuss the case of probability distortions
and introduce the concept of forward rank-dependent performance processes
. Among others\, I will show how forward probability distortions are affec
ted by “real-time” changes in the stochastic environment and\, also\, pres
ent a striking equivalence between forward rank-dependent criteria and tim
e-monotone forward processes under appropriate measure-changes. A byproduc
t of the work is a novel result on the so-called dynamic utilities and on
time-inconsistent problems in the classical (backward) setting. \n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-12817@engineering.jhu.edu/ams
DTSTAMP:20190715T220524Z
CATEGORIES:
CONTACT:
DESCRIPTION:Title: Uncertainty propagation in mechanics and materials by de
sign based on surrogate model development\nAbstract: With the onset of adv
anced manufacturing capabilities and in situ characterization techniques\,
simultaneous material/structural design is becoming increasingly feasible
for maximum structural performance. At the heart of such design processes
is the availability of multi-scale mechanics models that incorporate expl
icit representation of the material (such as microstructural descriptors)
and the structure (such as the geometry). A major challenge here is that a
full physically-based multi-scale model is often computationally infeasib
le. Surrogate functions that provide a simplified representation of the ma
terial provide a much more efficient alternative. Such surrogate functions
also enable a quantification of the propagation of uncertainties between
scales. While these surrogate functions do increase efficiency\, they lead
to a number of associated challenges. If the material is represented by a
large number of microstructural parameters\, then the high dimensionality
of the surrogate function requires many samples in order to build an accu
rate surrogate. Furthermore\, some micro-scale behavior\, such as sudden d
amage\, can lead to discontinuities in the surrogate function\, which make
s it difficult to interpolate or collocate the results. This seminar will
describe a number of approaches to building surrogates\, including cases i
n which the micro-scale model provides key response values and/or gradient
s of key response values.
DTSTART;TZID=America/New_York:20190418T133000
DTEND;TZID=America/New_York:20190418T143000
SEQUENCE:0
SUMMARY:AMS Seminar: Lori Brady (JHU-Civil Eng) @ Whitehead 304
URL:https://engineering.jhu.edu/ams/events/ams-seminar-lori-brady-jhu-civil
-eng-whitehead-304/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n

**Title
: **Uncertainty propagation in mechanics and materials by design ba
sed on surrogate model development

**Abstract: **With
the onset of advanced manufacturing capabilities and in situ characteriza
tion techniques\, simultaneous material/structural design is becoming incr
easingly feasible for maximum structural performance. At the heart of such
design processes is the availability of multi-scale mechanics models that
incorporate explicit representation of the material (such as microstructu
ral descriptors) and the structure (such as the geometry). A major challen
ge here is that a full physically-based multi-scale model is often computa
tionally infeasible. Surrogate functions that provide a simplified represe
ntation of the material provide a much more efficient alternative. Such su
rrogate functions also enable a quantification of the propagation of uncer
tainties between scales. While these surrogate functions do increase effic
iency\, they lead to a number of associated challenges. If the material is
represented by a large number of microstructural parameters\, then the hi
gh dimensionality of the surrogate function requires many samples in order
to build an accurate surrogate. Furthermore\, some micro-scale behavior\,
such as sudden damage\, can lead to discontinuities in the surrogate func
tion\, which makes it difficult to interpolate or collocate the results. T
his seminar will describe a number of approaches to building surrogates\,
including cases in which the micro-scale model provides key response value
s and/or gradients of key response values.

**Title: **Mo
deling Particulate Air Pollution for Inference About Neurodegenerative Eff
ects

**Abstract: **Evidence is accumulating to suppor
t a link between chronic air pollution exposures and neurotoxic effects.
For instance\, EPA’s most recent Integrated Science Assessment for particu
late matter concluded that the associations between PM_{2.5} and n
ervous system effects\, including brain inflammation\, oxidative stress\,
reduced cognitive function\, and neurodegeneration\, are likely causal. W
e are conducting an epidemiologic cohort study\, the Adult Changes in Thou
ght Air Pollution (ACT-AP) study\, to determine whether\, in an elderly po
pulation free of dementia at baseline\, long-term air pollution exposure i
s associated with cognitive decline\, incidence of Alzheimer’s disease and
all-cause dementia\, and adverse neuruopathological changes in brain tiss
ue. For exposure assessment in this study we are modeling criteria air po
llutants using existing regulatory monitoring data supplemented with measu
rements from low-cost sensors. One important scientific question we are a
ddressing is whether low-cost sensor data improve our ability to quantify
PM_{2.5 }exposure in the Puget Sound. I will discuss our approach
and our preliminary conclusions that suggest that low-cost sensor can imp
rove exposure assessment in epidemiologic cohort studies. I will also des
cribe the innovative mobile monitoring campaign we have just started. We
designed this campaign with epidemiologic inference in mind\; it will all
ow us to estimate whether there are adverse effects to the brain associate
d with infrequently monitored traffic-related pollutants\, including ultra
fine particles and black carbon.

**Bio: **Dr. Sheppar
d is Professor and Assistant Chair of Environmental and Occupational Healt
h Sciences and Professor of Biostatistics. Her current research portfolio
includes several studies of air pollution exposures and their neurotoxican
t effects. She has a Ph.D. in biostatistics. Her methodologic interests ce
nter on observational study methods\, exposure modeling\, and epidemiology
\, and\; her applied research focuses on the the health effects of occupat
ional and environmental exposures. She is principal investigator of a NIH-
funded training grant called Biostatistics\, Epidemiologic & Bioinformatic
s Training in Environmental Health and SURE-EH\, a project to promote dive
rsity in the environmental health sciences. She leads the biostatistical c
ores for several projects and collaborates with DEOHS faculty on air pollu
tion cohort studies\, identifying the effects of multipollutant exposures\
, and studying manganese exposures. She is a member of the Epidemiology ed
itorial board\, the Health Effects Institute Review Committee\, the EPA Cl
ean Air Scientific Advisory Committee \, and has served on the several EPA
Scientific Advisory Panels\, most recently for the Carcinogenic Potential
of Glyphosate. Board Chemical Assessment Advisory Committees for Ethylene
Oxide Review and for Toxicological Review of Libby Amphibole Asbestos.

**Title
: **Enter the matrix: interpreting biological systems through matri
x factorization and transfer learning of single cell data

**
Abstract: **Next generation and single cell sequencing have ushered
in an era of big data in biology. These data present an unprecedented op
portunity to learn new mechanisms and ask unasked questions. Matrix facto
rization (MF) techniques can reveal low-dimensional structure from high-di
mensional data to uncover new biological knowledge. The knowledge of gain
ed from low dimensional features in training data can also be transferred
to new datasets to relate disparate model systems and data modalities. We
illustrate the power of these techniques for interpretation of high dimen
sional data through case studies in postmortem tissues from GTEx\, acquire
d therapeutic resistance in cancer\, and developmental biology.