## Seminars

**
**The Department hosts weekly colloquia at 1:30 p.m. every Thursday.

## Endowed Lectures

Three endowed lecture series bring leading researchers and scientists from academia, government, and more, to campus every year.

## AMS Seminars

### Past Seminars

2023 – Mateo Diaz, Johns Hopkins University – Clustering a mixture of Gaussians with unknown covariance

2023 – Luana Ruiz, Johns Hopkins University – Manifold neural networks for large-scale geometric information processing

2023 – Ewan Davies, Colorado State University – Counting independent sets in dense bipartite graphs

2023 – Nicolas Trillos, University of Wisconsin Madison – Adversarial machine learning and clustered federated learning: a collective dynamics perspective

2023 – Jincheng Yang, University of Chicago – Recent developments in the Navier-Stokes equation

2023 – Javier Pena, Carnegie Mellon University – On the affine invariance of the conditional gradient algorithm

2023 – Ilias Zadik, Yale University – Revisiting the Metropolis Process for the Planted Clique Model Revisiting Process

2023 – Chunmei Wang, University of Florida – Finite expression methods for discovering physical laws from data

2023 – Richard M. Low, San Jose State University – ZP-magic graph labelings and the combinatorial nullstellensatz

2023 – Lorin Crawford, Microsoft Research New England – Probabilistic methods to identify multi-scale enrichment in genomic sequencing studies

2023 – Amin Karbasi, Yale University – When we talk about reproducibility, what are we talking about?

2024 – Aïda Ouangraoua, Université de Bordeaux – Transcript Homology Relationships and Phylogeny Reconstruction

2024 – Wei Zhu, University of Massachusetts Amherst – Symmetry-Preserving Machine Learning: Theory and Applications

2024 – Eric Vanden-Eijnden, New York University – Scientific Computing in the Age of Generative AI

2024 – Jeff Calder, University of Minnesota – PDEs and graph-based semi-supervised learning

2024 – Govind Menon, Brown University – A Bayesian View of Geometry

2024 – Christopher Musco, NYU Tandon School of Engineering – The Lanczos method, matrix functions, and the quest for optimality

2024 – Zhengling Qi, The George Washington University – Offline Data-Driven Decision-Making: Some Challenges and Solutions

2024 – Cristopher Moore, Sante Fe Institute – The physics of inference, phase transitions, and community detection

2024 – Jared Tanner, University of Oxford Mathematics Institute – Deep neural network stability at initialization: Nonlinear activations impact on the Gaussian process

2024 – Agostino Capponi, Columbia University – Virtual Trading in Multi-Settlement Electricity Markets

2024 – Marc A. Suchard, University of California, Los Angeles – Stochastic Compartmental Models for Infectious Disease Dynamics without Tears

2024 – Jeremias Sulam, Johns Hopkins University – Yay, my deep network works! But.. what did it learn?

2024 – Krishnakumar Balasubramanian, University of California, Davis – Geometry-aware algorithms for statistical data science

2024 – Francesco Sanna Passino, Imperial College London – Low-rank models for dynamic multiplex graphs and vector autoregressive processes

### Past Seminars

2022 – Kevin Leder, University of Minnesota – Computational techniques for understanding heterogeneous tumors

2022 – Haihao Lu, University of Chicago – First Order Methods for Linear Programming: Theory, Computation, and Applications

2022 – Evelyn Sander, George Mason University – Rigorous bifurcation methods for diblock and triblock copolymer models

2022 – Edinah Gnang, Johns Hopkins University – The composition lemma and its application in graph theory

2022 – Anthony Yezzi, Georgia Institute of Technology – Accelerated Gradient Descent in the PDE Framework

2022 – Victor Bailey, Georgia Institute of Technology – Frames via Unilateral Iterations of Bounded Operators

2022 – Maria Cameron, University of Maryland – Quantifying rare events with the aid of diffusion maps

2022 – Miranda Holmes-Cerfon, New York University – Numerically simulating particles with short-ranged interactions

2022 – Fei Liu, Johns Hopkins University – Statistical learning and inverse problems for interacting particle systems

2022 – Philip Thompson, Purdue University – A spectral least-squares-type method for heavy-tailed corrupted regression with unknown covariance & heterogeneous noise

2023 – Santanu Dey, Georgia Technology – Theoretical and computational analysis of sizes of branch-and-bound trees

2023 – James Schmidt, Applied Physics Laboratory Johns Hopkins University – Relations of Dynamical Systems through their Maps

2023 – Natalia Trayanova, Johns Hopkins University – AI-Powered Personalized Computational Cardiology

2023 – Carina Curto, Pennsylvania State University – An introduction to threshold-linear networks for neuroscience

2023 – Vince Lyzinski, University of Maryland – Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels

2023 – Ben Grimmer, Johns Hopkins University – Scalable, Projection-Free Optimization Methods

2023 – Joshua Agterberg, Johns Hopkins University – Estimating Higher-Order Mixed Memberships via the Two to Infinity Tensor Perturbation Bound

2023 – Yannis Kevrekidis, Johns Hopkins University – Data and the modeling of complex dynamical systems

2023 – Luhao Zhang, University of Texas at Austin – Model uncertainty, robust control, and costly information acquisition

2023 – Qiuqi Wang, University of Waterloo – E-backtesting risk measures

2023 – Haoyang Cao, Centre de Mathématiques Appliquées (CMAP), École Polytechnique –Bridging GANs and Stochastic Analysis

2023 – Yu Wang, University of Florida – Statistical verification algorithms for logical specifications on autonomous systems

2023 – Dennice Gayme, Johns Hopkins University – Toward wind farm control for power tracking

### Past Seminars

2021 – James Fill, Johns Hopkins University – Breaking Multivariate Records

2021 – Aki Nishimura, Johns Hopkins University – Bayesian sparse regression for large-scale observational health data

2021 – Brittany Hamfeldt, New Jersey Institute of Technology – Numerical Optimal Transport on the Sphere

2021 – Nadia Drenska, Johns Hopkins University – A PDE Interpretation of Prediction with Expert Advice

2021 – Alberto Bietti, New York University – On the Sample Complexity of Learning under Invariance and Geometric Stability

2021 – Dimitrii Ostrovskii, University of Southern California – Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain

2021 – Tom Fletcher, University of Virginia – The Riemannian Geometry of Deep Neural Networks

2021 – Eric Tchetgen, University of Pennsylvania – Proximal Causal Inference

2021 – Stephanie Hicks, Johns Hopkins University – Scalable statistical methods and software for single-cell data science

2021 – Dustin Mixon, Ohio State – Neural collapse with unconstrained features

2021 – Bruno Jedynak, Portland State University – A Convergent RKHS Algorithm for Estimating Nonparametric ODEs

2021 – Clare Lau, Johns Hopkins University Applied Physics Laboratory – Wasserstein Gradient Flows for Potentials in Frame Theory

2022 – Alex Wein, Georgia Institute of Technology – Understanding Statistical-vs-Computational Tradeoffs via Low-Degree Polynomials

2022 – Mike Dinitz, Johns Hopkins University – Faster Matchings via Learned Duals

2022 – Maxim Bichuch, Johns Hopkins University – Deep PDE Solution of BSDE

2022 – Yufei Zhao, Massachusetts Institute of Technology – Equiangular lines and eigenvalue multiplicities

2022 – Ian Tobasco, University of Illinois Chicago – The many, elaborate wrinkle patterns of confined elastic shells

2022 – Jasmine Foo, University of Minnesota Twin Cities – Spatial evolution and phenotypic switching in cancer

2022 – Mike Dinitz, Johns Hopkins University – Faster Matchings via Learned Duals

2022 – Christian Kuemmerle, Johns Hopkins University – Iteratively Reweighted Least Squares: New Formulations and Guarantees

2022 – Monika Nitsche, University of New Mexico – Evaluating near-singular integrals with application to vortex sheet and multi-nested Stokes flow

2022 – Jose Perea, Northeastern University – DREiMac: Dimensionality Reduction with Eilenberg-MacLane Coordinates

2022 – Michael Perlmutter, University of California, Los Angeles – Deep Learning on Graphs and Manifolds

2022 – Baba Vemuri, University of Florida – Nested Homogeneous Spaces: Construction, Learning and Applications

2022 – Julien Guyon, Bloomberg L.P., New York – Dispersion-Constrained Martingale Schrödinger Problems and the Joint S&P 500/VIX Smile Calibration Puzzle

2022 – Tyrus Berry, George Mason University – Beyond Regression: Operators and Extrapolation in Machine Learning

2022 – Maxim Bichuch, Johns Hopkins University – Introduction to Decentralized Finance

2022 – Cencheng Shen, University of Delaware – Graph Encoder Embedding

## AMS Endowed Lectures

The Alan Goldman Lecture Series in Operations Research was established in 1999 to honor the highly respected professor when he was named Professor Emeritus at JHU.

### About Alan J. Goldman

Alan J. Goldman was an expert in operations research – the use of mathematics to improve decisions on the design and operation of complex systems – whose favorite application areas included facility siting, transportation systems, and mathematical game theory. Goldman received his BA from Brooklyn College in mathematics and physics in 1952. He earned his MA and PhD in mathematics from Princeton University in 1954 and 1956, respectively. His dissertation area was topology, and the title of his dissertation is A Cech Theory of Fundamental Groups and Covering Spaces. From 1956 to 1961 he was an evening lecturer at American University and Catholic University of America, but his principal pre-JHU affiliation was with the National Bureau of Standards (now the National Institute of Standards and Technology), where he was founder and chief of operations research and also deputy chief of applied mathematics. Goldman joined Hopkins in 1979; earned the status of Professor Emeritus in 1999 and continued to teach until his death in 2010.

### Past Lectures

2024 – David Blei, Columbia University – Scaling and Generalizing Approximate Bayesian Inference

2022 – Anna Gilbert, Yale University – Metric Representations: Algorithms and Geometry

2021 -Maria Chudnovsky, Princeton University – Induced Subgraphs and Tree Decompositions

2020 – Rekha Thomas, University of Washington, Seattle – “Lifting for Simplicity: Concise Descriptions of Convex Sets” Goldman Lecture 11-19-2020(pdf)

2019 – Jack Edmonds, “Matroids and Optimum Branching Systems” edmonds goldman – slides

2019 – Gerard Cornuejols, Carnegie Mellon University – “Min-Max Relations for Packing and Covering”

2018 – Bill Cook, Johns Hopkins University

2016 – Jorge Nocedal, Northwestern University – “Stochastic Newton Methods for Machine Learning” goldman-lecture-slides

2015 – Daniel Bienstock, Columbia University – “Recent Results on Polynomial Optimization Problems”

2014 – Stephen Wright, University of Wisconsin-Madison – “The Revival of Coordinate Descent Methods” SLIDES – Video of Lecture

2013 – Michael Todd, Cornell University – “Exponential Gaps in Optimization Algorithms”

2013 – David Shmoys, Cornell University – “Improving Christofides’ Algorithm for the s-t Path Traveling Salesman Problem”

2011 – Dimitris Bertsimas, Massachusetts Institute of Technology – “A Computationally Tractable Theory of Performance Analysis in Stochastic Systems”

2010 – Arthur Benjamin, Harvey Mudd College – “Combinatorial Trigonometry”

2009 – Richard Francis, University of Florida – “Aggregation Error for Location Models: Survey and Analysis”

2009 – Lisa Fleischer, Dartmouth University – “Submodular Approximation: Sampling-based Algorithms and lower Bounds”

2007 – Eva Tardos, Cornell University – “Games in Networks”

2006 – Christine Shoemaker, Cornell University – “Optimization, Calibration, and Uncertainty Analysis of Multimodal, Computationally Expensive Models with Environmental Applications”

2005 – George Nemhauser, Georgia Institute of Technology – “Scheduling an Air Taxi Service”

2004 – Karla Hoffman, George Mason University

2000 – Tom Magnanti, MIT

1999 – Alan J. Goldman, Johns Hopkins University – “Reflections and Translations”

The Acheson J. Duncan Lecture Series

In 1986, an anonymous donor established the Acheson J. Duncan Distinguished Visitor Fund to honor the internationally recognized leader in quality control and industrial statistics. The endowment supports an annual visit and lecture by a distinguished mathematical scholar.

### About Acheson J. Duncan

Acheson J. Duncan spent 25 years as a faculty member at Johns Hopkins. His extensive writings in the field include the text, *Quality Control and Industrial Statistics*, published in 1952 and now in its fifth edition with several international translations. The late dean of the Whiting School of Engineering, Robert H. Roy noted that Duncan and his work were revered in Japan, where Duncan frequently lectured in the years following World War II. A native of New Jersey, Duncan received his PhD in economics from Princeton in 1936, and was a faculty member there for 13 years before coming to Hopkins. Duncan died in 1995 at age 90.

### Past Lectures

2023 – Leslie Greengard, New York University – Adaptive methods for the simulation of diffusion in complex geometries

2023 – Karen Willcox, The University of Texas at Austin – Learning physics-based models from data: Perspectives from projection-based model reduction

2020 – Kavita Ramanan, Brown University- “Beyond Mean-field Limits for Large-scale Stochastic Systems” Duncan Flyer Lecture 12-3-2020

2019 – Susan Murphy, Harvard University- “Online Experimentation and Learning Algorithms in a Clinical Trial”

2019 – Rina Foygel Barber, University of Chicago- “Robust inference with the knockoff filter.”

2018 – Stuart Geman, Brown University- “Real and Artificial Neural Networks”

2017 – René Carmona, Princeton University- “Mean Field Games with Major and Minor Players: Theory and Numerics”

2016 – Bin Yu, University of California – “Movie Reconstruction from Brain Signals : Mind-Reading”

2014 – Laurent Saloff-Coste, Cornell University – “Groups and Random Walks” and “Random Walk Invariants of Groups”

2013 – David Siegmund, Stanford University – “The Intersection of Operations Research, Kinetic Theory, and Genetics” and “Detection of Local Signals in Genomics”

2012 – Gerard Ben Arous, New York University – “Counting Critical Points of Random Functions of Many Variables” and “RMT^2: Random Morse Theory Meets Random Matrix Theory”

2011 – Joel Zinn, Texas A&M University – “A Meandering ‘Trip’ through High Dimensions” and “Limit Theorems in High Dimensions”

2010 – Andreas Buja, University of Pennsylvania – “Seeing is Believing: Statistical Visualization for Teaching and Data Analysis” and “Statistical Inference for Exploratory Data Analysis and Model Diagnostics”

2009 – Jonathan Taylor, Stanford University – “Deformation Based Morphometry, Random Fields and Multivariate Linear Models” and “Integral Geometry of Random Level Sets”

2008 – Yali Amit, University of Chicago – “Statistical Models in Computer Vision” and “Estimation of Deformable Object Models”

2007 – Robert Azencourt, University of Houston – “Automatic Learning and Multi-Sensors Diagnosis” and “Ultrasound Image Analysis: Speckle Tracking for Recovery of Cardiac Motion”

2006 – Lawrence A. Shepp, Rutgers University – “Applications of Convexity” and “Problems in Convexity”

2005 – Gregory F. Lawler, Cornell University – “Random Walks: Simple and Self-Avoiding” and “Conformal Invariance, Brownian Loops, and Measures on Random Paths”

2004 – Leo Breiman, University of California, Berkeley – “Random Forests: A Statistical Tool for the Sciences” and “Statistics, Machine Learning, and Data Mining”

2003 – Oded Schramm, Microsoft Research – “Emergence of Symmetry: Conformal Invariance of Scaling Limits of Random Systems” and “Random Triangulations”

2002 – Steven E. Shreve, Carnegie-Mellon University – “Probability Models for Derivative Securities” and “A Unified Model for Credit Derivatives”

2001 – David Donoho, Stanford University – “Interactions Between Data Analysis of Natural Images, Biological Vision, and Mathematical Analysis” and “Beyond Wavelets: Ridgelets, Curvelets, Beamlets”

2000 – Roger J-B Wets, University of California, Davis – “Limit Theorems for Random Lower Semicontinuous Functions with Applications to Statistics, Stochastic Optimization, Probability, and Stochastic Homogenization” and “Stability Issues for Equilibrium Points”

1999 – Ken Alexander, University of Southern California – “Power-Law Corrections to Exponential Decay of Correlations and Connectivities in Lattice Models” and “Droplets and Bubbles: The Mathematical Description of Phase Separation”

1998 – David Pollard, Yale University – “Some Statistical Issues in the Construction of Jury Arrays” and “What is Randomization?”

1997 – Rick Durrett, Cornell University

1996 – Michael Saks, Rutgers University – “Randomness as a Scarce Resource” and “Extractors, Dispersers, and Pseudorandom Generators”

1995 – Michael J. Todd, Cornell University

1994 – David J. Aldous, University of California, Berkeley

1993 – Rudolph Beran, University of California, Davis

1992 – Peter Ney, University of Wisconsin

1991 – Paul D. Seymour, University of Waterloo

1990 – Persi Diaconis, Harvard University

1989 – Ralph L. Disney, Texas A&M University

The John C. and Susan S. G. Wierman Lecture Series in Air Quality Data Analysis features talks on developments in air quality data analysis that are relevant for policy development. It seeks to bring together faculty and researchers in engineering and natural sciences with state and local air quality officials, to enhance understanding and stimulate collaboration on important air quality issues. The lectures are intended to showcase new developments, to encourage the quantitative analysis of scientific issues related to air quality, and to elucidate the policy implications of recent research. The lecture series was established with a permanent endowment by Prof. John C. Wierman and Susan S. G. Wierman.

### About the Sponsors

John C. Wierman, a professor of Applied Mathematics and Statistics at Johns Hopkins University since 1981, served as department chair from 1988 to 2000. The founder of the W. P. Carey Program in Entrepreneurship & Management, he was director of the program and its successor, the Center for Leadership Education, from 1996 until 2009. His mathematical research is published in probability, discrete mathematics, and statistics journals, with applied articles in physics, computer science, molecular biology, education, and business journals. He received his BS. and PhD from the University of Washington and is a Fellow of the Institute of Mathematical Statistics and the Institute of Combinatorics and its Applications.

Susan S.G. Wierman was executive director of Mid-Atlantic Regional Air Management Association from 1996 to 2017, where she worked to improve regional air quality. She earned urban planning degrees from the University of Washington, and a certificate in Continuing Engineering Studies from Johns Hopkins University. She is a Fellow of the international Air and Waste Management Association, and was the 2012 recipient of its S. Smith Griswold Outstanding Air Pollution Official award.

### Past Lectures

2023 – Cory Zigler, University of Texas at Austin – “Causal Inference in Air Quality Regulation: An Overview and Two Topics in Statistics and Machine Learning”

2022 – Susan Anenberg, George Washington University – “Climate change, air pollution, and public health impacts: From science to policy”

2021 – Roger Peng, Johns Hopkins Bloomberg School of Public Health – “Statistical Approaches to Studying Air Pollution Mixtures and Health”

2020 – Dr. Doug Dockery, Harvard University – “Air Pollution Accountability Studies: Lessons Learned and Future Opportunities”

2019 – Dr. Lianne Sheppard, University of Washington – “Modeling Particulate Air Pollution for Inference About Neurodegenerative Effects”

2017 – Brian Duncan, NASA Goddard Space Flight Center – “The Growing Importance of Satellite Data for Air Quality Applications”

2017 – Amy Herring, University of North Carolina Chapel Hill – “Spatial-temporal Modeling of the Association between Air Pollution Exposures and Birth Outcomes: Identifying Critical Exposure Windows”

2015 – Francesca Dominici, Harvard University – “Comparative Effectiveness Research of Environmental Exposures: Connecting the Dots with Big Data”

2014 – Michelle Bell, Yale University – “Exposure to Air Pollution during Pregnancy and Risk of Adverse Birth Outcomes”

2013 – Montse Fuentes, North Carolina State University – “Calibration of deterministic numerical models using nonparametric spatial density functions”

2012 – Richard L. Smith, University of North Carolina, Chapel Hill/SAMSI – “Attribution of Extreme Climatic Events”

2011 – C. Arden Pope III, Brigham Young University – “Human Health Effects of Air Pollution” Statistics and Public Policy”

2009 – Katherine Bennett Ensor, Rice University – “Houston Air Quality: A Simultaneous Examination of Multiple Pollutants”

2008 – William Christensen, Brigham Young University – “Identifying Pollution Source Locations for Air Quality Monitoring”

2008 – Barry D. Nussbaum, US Environmental Protection Agency – “Greenhouse, White House, and Environmental Statistics: The Use of Statistics in Environmental Decision Making”

2006 – William F. Hunt, Jr., North Carolina State University – “Environmental Statistics: A New Source of Discovery for Tomorrow’s Problem-Solvers”

2004 – Philip K. Hopke, Clarkson University – “Advanced Factor Analysis Methods for Receptor Modeling”

### Past Lectures

2023 – Gitta Kutyniok, Ludwig-Maximilians Universität München – Reliable AI: Successes, Challenges, and Limitations

2023 – Deanna Needell, The University of California, Los Angeles – Towards Transparency, Fairness, and Efficiency in Machine Learning

2023 – Edo Airoldi, Temple University – Designing experiments on social, healthcare, and information networks