Monday / CS Seminar
February 10th / 12:00 PM
Hybrid / Mudd 3514
Speaker
Sidhanth Mohanty, MIT
Talk Title
A quest for an algorithmic theory for high-dimensional statistical inference
Abstract
"When does a statistical inference problem admit an efficient algorithm?
There is an emergent body of research that studies this question by trying to understand the power and limitations of various algorithmic paradigms in solving statistical inference problems; for example, convex programming, Markov chain Monte Carlo (MCMC) algorithms, and message passing algorithms to name a few.
Of these, MCMC algorithms are easy to adapt to new inference problems and have shown strong performance in practice, which makes them promising as a universal algorithm for inference. However, provable guarantees for MCMC have been scarce, lacking even for simple stylized models of inference.
In this talk, I will survey some recent strides that I have made with my collaborators on achieving provable guarantees for MCMC in inference, and some new tools we introduced for analyzing the behavior of slow-mixing Markov chains."
Biography
"Sidhanth is broadly interested in theoretical computer science and probability theory, and his primary interests are on the algorithms and complexity of statistical inference, and spectral graph theory.
Sidhanth is currently a postdoctoral researcher at MIT, hosted by Sam Hopkins. Previously, he received his PhD in Computer Science at UC Berkeley in 2023 where he was advised by Prasad Raghavendra."
Research/Interest Areas
Theoretical computer science, algorithmic statistics, analysis of Markov chains, spectral graph theory, semidefinite programming, random matrix theory
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Zoom: https://northwestern.zoom.us/j/98400432043?pwd=t4PaTy1pIWxa9R0QrhxojIjKeJ8pho.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d9a67624-ddf1-4295-bb3a-b27b011768d7
Community Connections Topic: Lab counterculture
Cost: free
Wynante R Charles
(847) 467-8174
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