Vidya Muthukumar is an Assistant Professor in the Schools of Electrical and Computer Engineering, and Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Muthukumar’s broad interests are in game theory, online and statistical learning. She is particularly interested in designing learning algorithms that provably adapt in strategic environments, fundamental properties of overparameterized models, and fairness, accountability, and transparency in machine learning.
Dr. Muthukumar received the B.Tech (with honors) degree from the Indian Institute of Technology, Madras and the Ph.D. degree in Electrical Engineering from University of California, Berkeley. She interned at IBM Research in the summer of 2018 as a Science for Social Good fellow. Before joining Georgia Tech, she spent a semester at the Simons Institute for the Theory of Computing as a research fellow for the program “Theory of Reinforcement Learning.” She is the recipient of a Simons-Berkeley Google Research Fellowship, IBM Science for Social Good Fellowship, and the UC Berkeley EECS Outstanding Course Development and Teaching Award. Dr. Muthukumar serves on the senior program committee for COLT 2021 and ALT 2022.
In her spare time, Dr. Muthukumar enjoys singing Carnatic vocal music, playing the piano, and long-distance cycling.
- Jan 2022: Papers on inverse learning from an exploring demonstrator and harmless interpolation in kernel regression and classification accepted to AISTATS 2022. Thanks and special mention to lead authors (Wenshuo Guo and Andrew McRae respectively)!
- Dec 2021: Our work on equivalences between the multiclass SVM, one-vs-all SVM and minimum-2-norm interpolation will be presented at NeurIPS 2021. Thanks and congrats to my co-authors Ke Wang and Christos Thrampoulidis!
- Oct 2021: Our work on classification-vs-regression in overparameterized regimes is published in Journal of Machine Learning Research.
- Sept 2021: Excited to be a long-term participant in the Simons Institute Spring 2022 program on “Learning and Games”.
- Sept 2021: New survey paper on theory of overparameterized ML, jointly authored with Yehuda Dar and Rich Baraniuk. Comments are very welcome!
- Aug 2021: ECE 8803: Online Decision Making in Machine Learning, Fall 2021 now live! Check out the course webpage here (will be frequently updated).
- Aug 2021: Excited to be a Class of 1969 Teaching Fellow for the academic year 2021-2022!
- Apr 2021: Registration for “Theory of Overparameterized Machine Learning” workshop open! To be held April 20-21.