Mikhail Belkin is a Professor at Halicioglu Data Science Institute and Computer Science and Engineering Department at UCSD and an Amazon Scholar. Prior to that he was a Professor at the Department of Computer Science and Engineering and the Department of Statistics at the Ohio State University. He received his PhD from the Department of Mathematics at the University of Chicago (advised by Partha Niyogi). His research interests are broadly in theory and applications of machine learning, deep learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps, Graph Regularization and Manifold Regularization algorithms, which brought ideas from classical differential geometry and spectral graph theory to data science. His more recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. The empirical evidence necessitated revisiting some of the classical concepts in statistics and optimization, including the basic notion of over-fitting. One of his key findings has been the "double descent" risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation. His recent work focusses on understanding feature learning and over-parameterization in deep learning. Mikhail Belkin is a recipient of a NSF Career Award and a number of best paper and other awards. He had served on the editorial boards of IEEE Proceedings on Pattern Analysis Machine Intelligence and the Journal of the Machine Learning Research. He is currently the editor-in-chief of SIAM Journal on Mathematics of Data Science (SIMODS).
Modern Paradigms in Generalization, Fall 2024, Visiting Scientist
Summer Cluster: Deep Learning Theory, Summer 2022, Visiting Scientist and Program Organizer
Foundations of Deep Learning, Summer 2019, Visiting Scientist
Foundations of Machine Learning, Spring 2017, Visiting Scientist
Algorithmic Spectral Graph Theory, Fall 2014