Ph.D. student 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."

**Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints**

with Martin J. Wainwright and Lin Xiao.

[ arXiv ]

**Optimally tackling covariate shift in RKHS-based nonparametric regression**

with Cong Ma and Martin J. Wainwright.

[ arXiv ]
[ Annals of Statistics (to appear, 2023+) ]

**A new similarity measure for covariate shift with applications to nonparametric regression**

with Cong Ma and Martin J. Wainwright.

[ arXiv ]
[ ICML 2022 (proceedings)
(slides)
(poster)
(long oral)
]

**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 ]

**Weighted matrix completion from non-random, non-uniform sampling patterns**

with Simon Foucart, Deanna Needell, Yaniv Plan, and Mary Wootters.

[ arXiv ]
[ IEEE Transactions on Information Theory ]

**FedSplit: an algorithmic framework for fast federated optimization**

with Martin J. Wainwright.

[ arXiv ]
[ NeurIPS 2020]

**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.

**UC Berkeley**

Stat210B: Mathematical Statistics. GSI, Spring 2021.

**Stanford University**

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.

Cory Hall, BLISS Lab

pathakr@berkeley.edu

http://www.cs.berkeley.edu/~pathakr/

© Reese Pathak. Last modified: March 23, 2023.