I am a Postdoctoral researcher in the Department of Statistics and Data Science at CMU, working with Prof. Aaditya Ramdas. My research interests lie in the general areas of maching learning and sequential decision making. I am currently working on the problem of sequential nonparametric hypothesis testing. In the past, I have worked on the topics of Bayesian Optimization, Active Learning and Reinforcement Learning.
PhD in Electrical Engineering
University of California, San Diego
M.E. in Electrical Engineering
Indian Institute of Science, Bangalore
B.E. in Electrical Engineering
Indian Institute of Technology, Kharagpur
[01/22/21] Our paper Significance of Gradient Information in Bayesian Optimization got accepted at AISTATS 2021 (final version in preparation).
[11/20/20] Awarded the Shannon Memorial Fellowship by CMRR, UCSD for the academic year 2020/21.
[06/01/20] Our paper Adaptive Sampling for Learning Probability Distributions got accepted at ICML 2020.
[05/14/20] Our paper Active Model Estimation in Markov Decision Processes got accepted at UAI 2020.
Optimal scheme for allocating samples to learn $K$ distributions uniformly well.
Algorithm with uniformly improved regret bounds + a computationally efficient heuristic with better empirical performance
A minimax near-optimal active learning algorithm for classification with abstention.
Selected list of graduate coursework in ECE and Math departments
The term in bracket denotes the number of courses in the series.
I have served as a TA for the following courses.