External Events

Online Seminar on Mathematical Foundations of Data Science  

DATE: Friday, June 25th, 2021

TIME:  11:00 am EDT

LOCATION:  Zoom: https://psu.zoom.us/j/95512102924

SPEAKER: Benjamin Van Roy, Stanford University

TITLE: Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States

Abstract: I will present a simple reinforcement learning agent that implements an optimistic version of Q-learning and results establishing that this agent can operate with some level of competence in any environment.  The results apply even when the environment is arbitrarily complex — and much more so than the agent — and treat a general agent-environment interface, involving a single stream of experience.  This level of generality positions the results to inform the design of future agents for operation in complex real environments.  I will also discuss some open issues related to the agent and analysis.

Bio: Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research focuses on the design, analysis, and application of reinforcement learning algorithms. Beyond academia, he leads a DeepMind Research team in Mountain View, and has also led research programs at Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.

He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he co-edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, and the INFORMS Journal on Optimization.

He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tstitsiklis. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, and the Management Science and Engineering Department's Graduate Teaching Award. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe, the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University, a Visiting Professor at the National University of Singapore, and a Visiting Professor at the Chinese University of Hong Kong, Shenzhen.


Past Seminars

Online Seminar on Mathematical Foundations of Data Science

DATE: Friday, June 18th, 2021

TIME:  11:00 am EDT

LOCATION:  Zoom: https://psu.zoom.us/j/95512102924

SPEAKER: Michael Kosorok, University of North Carolina at Chapel Hill

TITLE: Some Recent Developments in Machine Learning for Addressing Multiple Outcomes in Data Driven Decision Support

Abstract: In this presentation, we will discuss some recent developments in machine learning based precision health for estimating dynamic treatment regimes which seek to balance two or more outcomes, such as, for example, balancing treatment efficacy against side effect burden. The first approach involves obtaining individual patient input on the relative priority of outcomes through preference instruments based on, for example, item response theory or discrete choice modeling. The second approach adapts inverse reinforcement learning to infer physician outcome priorities, allowing for the possibility of errors in decision making. Both approaches are illustrated with practical examples from mental health.

Bio: Michael R. Kosorok, PhD, is W. R. Kenan, Jr. Distinguished Professor of Biostatistics and Professor of Statistics and Operations Research at UNC-Chapel Hill. His research expertise is in biostatistics, data science, machine learning and precision medicine, and he has written a major text on the theoretical foundations of these and related areas in biostatistics (Kosorok, 2008, Springer) as well as co-edited (with Erica E. M. Moodie, 2016, ASA-SIAM) a research monograph on dynamic treatment regimes and precision medicine. He also has expertise in the application of biostatistics and data science to human health research, including cancer and cystic fibrosis. In particular, he is the contact principal investigator on an NCI program project grant (P01 CA142538), which focuses on statistical methods for novel cancer clinical trials in precision medicine, including biomarker discovery and dynamic treatment regimes. He has pioneered machine learning and data mining tools for these and related areas.

Thank you for attending last week's talk by Professor Morton! The recorded talk is available through:


Past Seminar
Online Seminar on Mathematical Foundations of Data Science  

DATE: Friday, June 4th, 2021

TIME:  11:00 am EDT

LOCATION:  Zoom: https://psu.zoom.us/j/95512102924

SPEAKER:: Stephen Wright, University of Wisconsin at Madison

Title: The Role of Complexity Bounds in Optimization

Abstract: Complexity analysis in optimization seeks upper bounds on the amount of work required to find approximate solutions of problems in a given class with a given algorithm, and also lower bounds, usually in the form of a worst-case example from a given problem class as regards the work required by a particular class of algorithms. The relationship between theoretical complexity bounds and practical performance of algorithms on “typical” problems varies widely across problem and algorithm classes, and relative interest among researchers between these two aspects of algorithm design and analysis has waxed and waned over the years. This talk surveys complexity analysis and its relationship to practice in optimization, with an emphasis on linear programming and convex and nonconvex nonlinear optimization, providing historical (and cultural) perspectives on research in these areas.

Bio: Stephen J. Wright holds the George B. Dantzig Professorship, the Sheldon Lubar Chair, and the Amar and Balinder Sohi Professorship of Computer Sciences at the University of Wisconsin-Madison. His research is in computational optimization and its applications to many areas of science and engineering. Prior to joining UW-Madison in 2001, Wright held positions at North Carolina State University (1986-90), Argonne National Laboratory (1990-2001), and the University of Chicago (2000-01). He has served as Chair of the Mathematical Optimization Society, and Trustee of SIAM. He is also a Fellow of SIAM. In 2014, he won the W.R.G. Baker award from IEEE. In 2020, he won the Khachiyan Prize from INOFRMS. Wright is the author and coauthor of widely used text/reference books in optimization, including Primal Dual Interior-Point Methods, and Numerical Optimization. He has published widely on optimization theory, algorithms, software, and applications. Wright is current editor-in-chief of the SIAM Journal on Optimization, and previously served variously as editor-in-chief or associate editor of Mathematical Programming (Series A), Mathematical Programming (Series B), SIAM Review, SIAM Journal on Scientific Computing, and several other journals and book series.




Title: An extreme value approach to CoVaR estimation
Date & Time: 4pm UTC on Wednesday 02 June 2021. (check your local time)
Recording: A recording will be available for 7 days.
Speaker: Natalia Nolde (University of British Columbia, Canada)
Zoom link: https://utoronto.zoom.us/j/88464563379

The global financial crisis of 2007-2009 highlighted the crucial role systemic risk plays in ensuring stability of financial markets. Accurate assessment of systemic risk would enable regulators to introduce suitable policies to mitigate the risk as well as allow individual institutions to monitor their vulnerability to market movements. One popular measure of systemic risk is CoVaR. We propose a methodology to estimate CoVaR semi-parametrically within the classical framework of multivariate extreme value theory. According to its definition, CoVaR can be viewed as a high quantile of a conditional distribution where the conditioning event corresponds to large losses of a given institution. We relate this conditional distribution to the tail dependence function. In the estimation procedure, we combine parametric modelling of the tail dependence function to address the issue of data sparsity in the joint tail regions and semi-parametric univariate high quantile estimation techniques. We prove consistency of the estimator, and illustrate its performance via simulation studies and a real data example. This is a joint work with Chen Zhou (Erasmus University) and Menglin Zhou (University of British Columbia).




Remote seminars – Statisfaction

A compilation of upcoming math seminars: Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale One World probability seminar One World Approximate Bayesian Computation On 


All seminars are on Tuesdays at 8:30 am PT (11:30 am ET / 4:30 pm London / 5:30 pm Berlin). 


We will use Zoom.Talk specific Zoom links will be advertised via the OWARS mailing list. The Zoom link will also be available on this google doc link 15min prior to a scheduled talk.


The talks will run every other week, and at least until the next SIAG/FME Biennial Meeting in June 2021  The talks will alternate with those set up by the Bachelier Finance Society 

All talks will be delivered remotely using Zoom. The talks are open to the public. Due to security reasons, all attendees have to register. The registration link will be posted on this web-site, next to the each seminar date below. The detailed information about each talk, and the registration link will be also distributed via SIAG/FME Mailing List.