Events

Upcoming Events

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Past Events

CSE DSI Machine Learning Seminar with Aaron Molstad (Statistics, UMN)

Prof. Aaron Molstad (UMN, Statistics) will speak on A direct approach to tree-guided feature aggregation for high-dimensional regression.

CSE DSI Machine Learning Seminar with Grani A. Hanasusanto (Industrial & Enterprise Systems Engineering, UIUC)

Grani A. Hanasusanto (ISE, UIUC) will give a talk entitled Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions.

CSE DSI Machine Learning Seminar with Lu Lu (Statistics, Yale)

Prof. Lu Lu (Yale) will give a talk entitled Learning neural operators accurately, efficiently, reliably, and in one shot.

CSE DSI Machine Learning Seminar with Aleksandr Aravkin (Applied Math, U Washington)

Aleksandr Aravkin (Applied Math, University of Washington) will speak on Fusing parametric and nonparametric estimation to obtain global health results using heterogeneous data.

CSE DSI Machine Learning Seminar with Kshitiz Upadhyay (AME, UMN)

Kshitiz Upadhyay (UMN, AEM) will give a talk on Data-Driven Mechanics for Soft Materials.

CSE DSI Machine Learning Seminar with Yisong Yue (Computing & Mathematical Sciences, Caltech)

Yisong Yue (Caltech) will give a talk on Design, Measure, Interpret: Foundation Models in the Scientific Loop.

CSE DSI Machine Learning Seminar with Stephen Wright (CS, UW Madison)

Optimization in Data Science

Optimization is vital to the modern revolution in data science. Techniques from optimization have become essential in formulating and solving a wide variety of problems in data analysis, machine learning, and AI. 

In turn, these areas have prompted a ferment of new research activity in optimization by posing challenging new problems and new contexts. We give a brief overview of the many problems in data science in which optimization provides the key solution methodology. We then focus on several areas of recent interest (including benign nonconvex optimization, robust optimization, matrix optimization, and neural networks), showing how the intersection of optimization and machine learning continues to stimulate research in both areas.
 
Stephen J. Wright is the George B. Dantzig Professor of Computer Sciences, Sheldon Lubar Chair of Computer Sciences, and Hilldale Professor at the University of Wisconsin-Madison. He recently served a term as Chair of the Computer Sciences Department. His research is in computational optimization and its applications to data science and many other areas of science and engineering.

Prior to joining UW-Madison in 2001, Wright held positions at North Carolina State University (1986-1990) and Argonne National Laboratory (1990-2001). He has served as Chair of the Mathematical Optimization Society (MOS) from 2007-2010 and was elected to the Board of Trustees of SIAM for the maximum three terms, from 2005-2014. He was elected to the National Academy of Engineering in 2024. In the same year, he received the George B. Dantzig Prize, awarded jointly by MOS and SIAM, for "original research having a major impact on mathematical optimization."  He has been invited to give a plenary lecture at ICM 2026. In 2020, he was awarded the Khachiyan Prize by the INFORMS Optimization Society for "lifetime achievements in the area of optimization," and also received the NeurIPS Test of Time Award.  He became a Fellow of SIAM in 2011. In 2014, he won the W.R.G. Baker Award from IEEE for best paper in an IEEE archival publication during 2009-2011.

Wright is the author / coauthor of widely used text and reference books in optimization including "Primal Dual Interior-Point Methods" and "Numerical Optimization." He has published broadly on optimization theory, algorithms, software, and applications.

Wright served from 2014-2019 as Editor-in-Chief of the SIAM Journal on Optimization and previously served as Editor-in-Chief of Mathematical Programming Series B. He has also served as Associate Editor of Mathematical Programming Series A, SIAM Review, SIAM Journal on Scientific Computing, and several other journals and book series.

CSE DSI Machine Learning Seminar with Bo Zeng (Industrial Engineering, Pitt)

Dr. Bo Zeng (University of Pittsburgh) will give a talk entitled Investigating Two-Stage Distributionally Robust Optimization from the Primal Perspective: A Complete and Intuitive Solution Framework.

CSE DSI Machine Learning Seminar with Atlas Wang (XTX Markets & UT Austin)

Atlas Wang (XTX Markets & UT Austin) will give a talk entitled Algorithmic Trading with Large-Scale Deep Learning.

CSE DSI to Hold Two GenAI Mixers in October 2025

CSE DSI will hold two GenAI Mixers on Friday, October 3, and Friday, October 17, 12:00-2:30PM, to foster collaboration and team formation for our current seed grant RFP.