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 an Amazon Research Award, NSF CAREER Award, Adobe Data Science Research Award, Simons-Berkeley Google Research Fellowship, and the UC Berkeley EECS Outstanding Course Development and Teaching Award. Dr. Muthukumar serves on the senior program committee for COLT 2021, COLT 2022 and COLT 2023.
In her spare time, Dr. Muthukumar enjoys singing Carnatic vocal music, playing the piano, and long-distance cycling.
Recent News
- Jan 2023: Received the Amazon Research Award with Ashwin Pananjady for “A framework for learning from online bidding”.
- Jan 2023: I am incredibly honored and grateful to have received the NSF CAREER Award! Read more about the project here.
- Jan 2023: Our work on the complexity of infinite-horizon general-sum stochastic games will be presented at Innovations in Theoretical Computer Science 2023. Thanks to my wonderful co-authors Yujia Jin and Aaron Sidford!
- Nov 2022: Congratulations to my student Guanghui Wang for winning the ARC-ACO Fellowship in the Spring 2023 cycle for his proposal on adaptive oracle-efficient online learning!
- Oct 2022: I’m making a poster presentation about my long-standing research interest in adaptive learning in the presence of strategic behavior at the DARPA Forward Conference, as a DARPA Riser! Read more about the award here.
- Oct 2022: Our work on adaptive oracle-efficient online learning will appear at NeurIPS 2022. Congratulations to lead author Guanghui Wang!
- Oct 2022: Grateful to the NSF for funding our upcoming 3-year-long effort on design principles and theory for data augmentation methods in machine learning (joint with Eva Dyer, Mark Davenport and Tom Goldstein)! Marking the beginning of this effort: our new preprint on understanding the effects of data augmentation on generalization of high-dimensional linear models.
- July 2022: Our work on universal and data-adaptive model selection in linear contextual bandits (joint with Akshay Krishnamurthy) to appear at ICML 2022.
- March 2022: Received the Adobe Data Science Research Award with Ashwin Pananjady for our joint work on inverse reward learning from a bandit demonstrator.
- Feb 2022: I’m co-organizing a mentorship workshop in conjunction with ALT 2022 for senior undergraduates and all-year graduates. Schedule here!
- Feb 2022: I’m listed on the Class of 1934 CIOS Honor Roll: Student Recognition of Excellence in Teaching for the new course that I introduced in Fall 2021, ECE 8803: Online Decision Making in Machine Learning.