Department of Electrical Engineering and Computer Science (EECS)
University of California, Berkeley
I am a PhD student at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). My advisors are Martin Wainwright and Mike Jordan. I am affiliated with the Berkeley AI Research (BAIR) lab.
My research interests span continuous optimization and high-dimensional statistics (and the many connections between these areas). More specifically, my recent work attempts to address–from a theoretical point of view–the statistical challenges encountered in modern learning problems: buzz-words include "distribution shift," "covariate shift," and "transfer learning."
Optimally tackling covariate shift in RKHS-based nonparametric regression
with Cong Ma and Martin J. Wainwright.
[ arXiv ]
Cluster-and-Conquer: A Framework For Time-Series Forecasting
with Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, and Inderjit S. Dhillon.
[ arXiv ]
On identifying and mitigating bias in the estimation of the COVID-19 case fatality rate
with Anastasios Angelopoulos, Rohit Varma, and Michael I. Jordan.
[ arXiv ] [ Harvard Data Science Review ] [ code ]
Note that the links below typically reflect current course offerings, and therefore do not represent the course that I helped teach. Nonetheless, I have included the links in case the context is helpful.
Stat210B: Mathematical Statistics. GSI, Spring 2021.
EE364a: Convex Optimization I. Instructor, Summer 2017. TA, Winter 2017 and 2018.
EE104: Introduction to Machine Learning. TA, Spring 2018.
EE103: Introduction to Matrix Methods. TA, Fall 2016, 2017, 2018.
CS106 (A, B, X): Introductory data structures and algorithms sequence. TA, many quarters.