ISyE Seminar: “Sparse Learning at Scale: "Convex, Mixed Integer Programming, and Statistical Perspectives”

Please join us for our next seminar via Zoom. This research-focused seminar will feature Rahul Mazumder from the Massachusetts Institute of Technology who will discuss using first-order convex optimization methods to solve regularized best-subset selection (BSS) problems.

3:30 p.m. - Graduate seminar
4:30 p.m. - Reception
Zoom link
Password: ISyE2021

*Required attendance for students in IE 8773 and 8774

About the seminar

Many fundamental high-dimensional statistics estimators, such as best-subset selection (BSS), can be naturally expressed as discrete optimization problems. Recently, mixed integer programming (MIP) methods have been shown to be promising candidates for formulating and solving small/moderate instances of these problems. This gives interesting insights into some less-understood statistical aspects of BSS, suggesting the need to design new estimators. On the computational front, current high-performance commercial integer programming solvers are somewhat black-box and can be challenging to scale to large instances.

About the speaker

Rahul Mazumder is the Robert G. James Career Development Associate Professor in the Operations Research and Statistics Group at Massachusetts Institute of Technology (MIT) Sloan School of Management. He is affiliated with the MIT Operations Research Center, MIT Center for Statistics, and MIT-IBM Watson AI Lab. His research interests are at the intersection of statistics and mathematical programming (convex and mixed integer optimization) and their applications to industry, the government, and the sciences.