About Myself:

The goal of my research is to build the foundations of reliable and trustworthy machine learning and carry them all the way to practice so that AI can truly bring about social good.  I am particularly interested in addressing the challenges around fairness, explainability, and privacy, by bringing in a novel foundational perspective deep-rooted in information theory, probability theory, causality, and optimization. 

Our work has appeared in several machine learning conferences, namely, NeurIPS, ICML, ICLR, AAAI, AISTATS as well as, information-theory venues, namely, ISIT and IEEE Transactions on Information Theory, featured in New Scientist and Montreal AI Ethics Brief, and also been adopted as part of the fair lending model review at JPMorgan. Our research group is supported by an NSF Career Award, a JPMorgan Faculty Award, and a Northrop Grumman Seed Grant.

Before joining UMD, I was a senior research associate at JPMorgan Chase AI Research in the Explainable AI Centre of Excellence (XAI CoE). I also received the Simons Institute Fellowship for the Causality Program in 2022.

I received my Ph. D. from Carnegie Mellon University. My thesis proposed a systematic quantification of the legally non-exempt disparity in machine learning models, bringing together causality, information theory, and law. I have received the K&L Gates Presidential Fellowship in Ethics and Computational Technologies for my research in this direction.  My research on quantifying accuracy-fairness tradeoffs using information theory (with IBM Research) was featured in New Scientist. My Ph.D. thesis received the A G Milnes Outstanding Thesis Award.

In my prior work, I also have examined problems in reliable computing, proposing solutions for large-scale distributed machine learning using tools from coding theory (an emerging area called “coded computing”). My results on coded computing have received substantial attention from across disciplines.

I am looking for motivated students to join my research group! 

Prospective Students: Please apply to the UMD Graduate Program and mention my name in your application.

Current Students: If you are already admitted to UMD, please send me an email with your resume and transcript.

Keywords:

• Trustworthy Machine Learning

• Fairness, Explainability, Privacy

• Information Theory

• Probability, Optimization, Causality

• Distributed Machine Learning, Coded Computing

• Performance Modeling and Queueing

• Natural Language Processing

• Compressive Sensing and Sparse Linear Algebra

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