Calendar

Feb
18
Thu
AMS Seminar w/ Genevera Allen (Rice University) on Zoom
Feb 18 @ 1:30 pm – 2:30 pm

Title: Data Integration: Data-Driven Discovery from Diverse Data Sources

Abstract: Data integration, or the strategic analysis of multiple sources of data simultaneously, can often lead to discoveries that may be hidden in individual analyses of a single data source.  In this talk, we present several new techniques for data integration of mixed, multi-view data where multiple sets of features, possibly each of a different domain, are measured for the same set of samples.  This type of data is common in healthcare, biomedicine, national security, multi-senor recordings, multi-modal imaging, and online advertising, among others. In this talk, we specifically highlight how mixed graphical models and new feature selection techniques for mixed, multi-view data allow us to explore relationships amongst features from different domains.  Next, we present new frameworks for integrated principal components analysis and integrated generalized convex clustering that leverage diverse data sources to discover joint patterns amongst the samples.  We apply these techniques to integrative genomic studies in cancer and neurodegenerative diseases to make scientific discoveries that would not be possible from analysis of a single data set.

Here is the new link and meeting ID+passcode:

https://wse.zoom.us/j/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09

Meeting ID: 914 6737 5713

Passcode: 272254

 

Feb
25
Thu
The John C. & Susan S.G. Wierman Lecture Series- AMS Seminar w/ Dr. Roger Peng (JHU Biostatistics) on Zoom
Feb 25 @ 1:30 pm – 2:30 pm
The John C. & Susan S.G. Wierman Lecture Series- AMS Seminar w/ Dr. Roger Peng (JHU Biostatistics) on Zoom

Title: Statistical Approaches to Studying Air Pollution Mixtures and Health

Abstract: The control of ambient air quality in the United States has been a major public health success since the passing of the Clean Air Act, with particulate matter (PM) reductions resulting in an estimated 160,000 premature deaths prevented in 2010 alone. Currently, public policy is oriented around lowering the levels of individual pollutants and this focus has driven the nature of much epidemiological research. Recently, attention has been given to viewing air pollution as a complex mixture and to developing a multi-pollutant approach to controlling ambient concentrations. We discuss current approaches to studying air pollution mixtures and detail their strengths and weaknesses. We also present a new statistical method for estimating the health effects of environmental mixtures using a mixture-altering contrast, which is any comparison, intervention, policy, or natural experiment that changes a mixture’s composition. As a demonstration, we apply this approach to assess the health effects of wildfire particulate matter air pollution in the Western United States.

Bio: Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health where his research focuses on the development of statistical methods for addressing environmental health problems. He has led some of the largest national studies on the health effects of ambient air pollution in the United States. Dr. Peng is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Dr. Peng is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health.

 

Here is the new link and meeting ID+passcode:

https://wse.zoom.us/j/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09

Meeting ID: 914 6737 5713

Passcode: 272254

Mar
4
Thu
AMS Seminar w/ Peyman Milanfar (Google) on Zoom
Mar 4 @ 1:30 pm – 2:30 pm

Title: Denoising as a Building Block: Form, function, and regularization of inverse problems

Abstract: Denoising of images has reached impressive levels of quality — almost as good as we can ever hope. There are thousands of papers on this topic, and their scope is so vast and approaches so diverse that putting them in some order is useful and challenging. I will speak about why we should still care deeply about this topic, what we can say about this general class of operators on images, and what makes them so special. Of particular interest is how we can use denoisers as building blocks for broader image processing tasks, including as regularizers for general inverse problems.

Bio: Peyman is a Principal Scientist / Director at Google Research, where he leads the Computational Imaging team. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. He was Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 he was on leave at Google-x, where he helped develop the imaging pipeline for Google Glass. Most recently, Peyman’s team at Google developed the digital zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution (“Super Res Zoom”) pipeline, and the RAISR upscaling algorithm.  In addition, the Night Sight mode on Pixel 3 uses our Super Res Zoom technology to merge images (whether you zoom or not) for vivid shots in low light.

Peyman received his undergraduate education in electrical engineering and mathematics from the University of California, Berkeley, and the MS and PhD degrees in electrical engineering from the Massachusetts Institute of Technology. He holds 15 patents, several of which are commercially licensed. He founded MotionDSP, which was acquired by Cubic Inc. (NYSE:CUB).

Peyman has been keynote speaker at numerous technical conferences including Picture Coding Symposium (PCS), SIAM Imaging Sciences, SPIE, and the International Conference on Multimedia (ICME). Along with his students, he has won several best paper awards from the IEEE Signal Processing Society.

He is a Distinguished Lecturer of the IEEE Signal Processing Society, and a Fellow of the IEEE “for contributions to inverse problems and super-resolution in imaging.”

http://www.milanfar.org

 

Here is the new link and meeting ID+passcode:

https://wse.zoom.us/j/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09

Meeting ID: 914 6737 5713

Passcode: 272254

 

 

Mar
11
Thu
AMS Seminar w/ Sara Del Valle (Los Alamos National Labs) on Zoom
Mar 11 @ 1:30 pm – 2:30 pm

Title: Real-time Data Fusion to Guide Disease Forecasting Models

Abstract: Globalization has created complex problems that can no longer be adequately understood and mitigated using traditional data analysis techniques and data sources. As such, there is a need for the integration of nontraditional data streams and approaches such as social media and machine learning to address these new challenges. In this talk, I will discuss how our team is applying approaches from the weather forecasting community including data collection, assimilating heterogeneous data streams into models, and quantifying uncertainty to forecast infectious diseases like COVID-19.  In addition, I will demonstrate that although epidemic forecasting is still in its infancy, it’s a growing field with great potential and mathematical modeling will play a key role in making this happen.

 

Here is the new link and meeting ID+passcode:

https://wse.zoom.us/j/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09

Meeting ID: 914 6737 5713

Passcode: 272254

Mar
18
Thu
AMS Seminar w/ Jelani Nelson ( University of California, Berkeley) on Zoom
Mar 18 @ 1:30 pm – 2:30 pm

Title: Memory lower bounds for sampling

Abstract: Suppose we would like to maintain a (multi)subset S of {1,…,n} dynamically subject to items being inserted into and deleted from S. Then when a user says “sample()”, we should return a (uniformly) random element of S, or an easier task, return just some (any) element in S. How much memory is required to accomplish this task? We answer
this question by giving an asymptotically optimal lower bound on the memory required.

Joint work with Michael Kapralov, Jakub Pachocki, Zhengyu Wang, David
P. Woodruff, and Mobin Yahyazadeh.

 

Here is the recording from the seminar:

https://wse.zoom.us/rec/share/XXuuP3BmXqWAExrgjkyYorfi0dYfSS9q1ldhiI_5gGGZAFHnBqyyDqLOO0IOrWwt.PHPHN5296_mER36h

Passcode: T&^!!4=C

Mar
25
Thu
AMS Seminar w/ Houman Owhadi (Caltech) on Zoom
Mar 25 @ 1:30 pm – 2:30 pm

Title: On learning kernels for numerical approximation and learning.

Abstract: There is a growing interest in solving numerical approximation problems as learning problems. Popular approaches can be divided into (1) Kernel methods, and (2) methods based on variants of Artificial Neural Networks. We illustrate the importance of using adapted kernels in kernel methods and discuss strategies for learning kernels from data.  We show how ANN methods can be formulated and analyzed as (1) kernel methods with warping kernels learned from data, and (2) discretized solvers for a generalization of image registration algorithms in which images are replaced by high dimensional shapes.

 

Here is the recording for the meeting:

https://wse.zoom.us/rec/share/icMPFsgd_Rsz0AK_w2SyxmXmtZ1LXnbJ7btTerUEERXVsMzqRIyMJ2_KqhD2IMWf.hvESTcAWBjAAwdg6

Passcode: ?tW.6T+%

 

Apr
1
Thu
AMS Seminar w/ Daniel Stein (New York University) on Zoom
Apr 1 @ 1:30 pm – 2:30 pm

Title: Nature vs. Nurture in Complex (and Not-So-Complex) Systems

Abstract: Understanding the dynamical behavior of many-particle systems following a deep quench is a central issue in both statistical mechanics and complex systems theory. One of the basic questions centers on the issue of predictability: given a system with a random initial state evolving through a well-defined stochastic dynamics, how much of the information contained in the state at future times depends on the initial condition (“nature”) and how much on the dynamical realization (“nurture”)? We discuss this question and present both old and new results for both homogeneous and random systems in low and high dimension.

Starting from next week, I’ll be taking over the seminar hosting duties from Amitabh, who is going on paternity leave. We’ll keep the zoom link and all other procedures exactly the same as they are now. Amitabh has created a well-oiled machine!

 

Here is the recording from the seminar above:

https://wse.zoom.us/rec/share/t0mGsIgM5fFxKqOSN-pR4b8YHGfVikbJ7DYP8NUkspDaSo4d3XPFE0gF7RxxtRib.-aaQTeF6uRB2tbQL

Passcode: ^R+Q1=r3

Apr
8
Thu
AMS Seminar w/ Davar Khosnevisan (University of Utah) on Zoom
Apr 8 @ 1:30 pm – 2:30 pm

Title: Phase Analysis of a Family of Reaction-Diffusion Equations

 

Abstract: We consider a reaction-diffusion equation driven by multiplicative space-time white noise, for a large class of reaction terms that include well-known examples such as the Fisher-KPP and Allen-Cahn equations. We prove that, in the “intermittent regime”: (1) If the equation is sufficiently noisy, then the resulting stochastic PDE has a unique invariant measure; and (2) If the equation is in a low-noise regime, then there are infinitely many invariant measures and the collection of all invariant measures is a line segment in path space. This gives proof to earlier predictions of Zimmerman et al (2000), discovered first through experiments and computer simulations.

This is joint work with Carl Mueller (University of Rochester) and Kunwoo Kim (POSTECH).

 

Here is the new link and meeting ID+passcode:

https://wse.zoom.us/j/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09

Meeting ID: 914 6737 5713

Passcode: 272254

Apr
15
Thu
AMS Seminar w/ Ed Scheinerman (JHU-AMS) on Zoom
Apr 15 @ 1:30 pm – 2:30 pm

Title: Finding a Compositional Square Root of Sine

Abstract: We consider the following type of problem: Given a function g : A ! A, find a
function f such that g = f  f . We are especially interested in the case sin : R ! R, but
consider the problem more broadly with results for other functions g defined on other sets
A. This is joint work with JHU undergraduate Tongtong Chen. And, despite appearances
to the contrary, this is a graph theory talk.

 

Here is the new link and meeting ID+passcode:

https://wse.zoom.us/j/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09

Meeting ID: 914 6737 5713

Passcode: 272254

Apr
29
Thu
AMS Seminar w/ Jim Gatheral (Baruch College) on Zoom
Apr 29 @ 1:30 pm – 2:30 pm

Title: Rough volatility: An overview

Abstract: The scaling properties of  historical volatility time series, which now appear to be universal,

motivate the modeling of volatility as the exponential of fractional Brownian motion. This model

can be understood as reflecting the high endogeneity of liquid markets and the long memory

of order flow.  The Rough Bergomi model, which is the simplest corresponding pricing model,

fits the implied volatility surface remarkably well.  As an application, we show how to forecast

realized variance.  We finish by presenting some more recent developments.

 

Here is the new link and meeting ID+passcode:

https://wse.zoom.us/j/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09

Meeting ID: 914 6737 5713

Passcode: 272254

Back to top