Ph. D. , Carnegie Mellon University
I recently completed my Ph. D. at CMU. I was fortunate to be advised by Prof. Pulkit Grover. I have also collaborated closely with Prof. Anupam Datta, Prof. Viveck Cadambe, Prof. Gauri Joshi, Prof. Tze Meng Low, and Dr. Kush Varshney.
Prior to joining CMU, I graduated from IIT Kharagpur with a B.Tech. in Electronics and Electrical Communication. My undergraduate thesis was advised by Prof. Arijit De. During my undergraduate studies, I received the Best Undergraduate Thesis Award and the HONDA Young Engineer and Scientist Award.
Broadly speaking, my research interests revolve around machine learning, information & coding theory, causality, and statistics. These days, I find myself particularly interested in fairness and explainability (recently published at AAAI'20, ICML'20 and featured in New Scientist and CMU Engineering News). I have received the K&L Gates Presidential Fellowship in Ethics and Computational Technologies and the CMU Cylab Presidential Fellowship for my research in this direction. My Ph.D. thesis received the A. G. Milnes Award from the ECE Department at CMU for the graduating batch of 2021.
In my prior work, I have also examined problems in reliable computing, proposing novel algorithmic solutions for large-scale machine-learning in the presence of faults and failures, using tools from coding theory (an emerging area called “coded computing”). My results on coded computing address problems that have been open for several decades and have received substantial attention from across communities (published at IEEE Transactions on Information Theory’19,’20, NeurIPS’16, AISTATS’18, IEEE BigData’18, ICML Workshop Spotlight’19, ISIT’17,’18, Proceedings of IEEE’20).
I will be starting as a researcher at JP Morgan Chase AI Research from July 2021.
I am looking forward to joining the Department of Electrical and Computer Engineering at the University of Maryland College Park as a tenure-track assistant professor from 2022.
Here's a link to my research statement.
Fairness, Explainability, Policy, Law
Information Theory, Coding Theory
Natural Language Processing
Distributed Machine Learning
Performance Modeling and Queueing
Compressive Sensing and Sparse Linear ALgebra