My research interests lie in the intersection of machine learning, information theory and signal processing. I leverage the tools from information theory and signal processing to develop theoretically justified learning algorithms with applications to fair machine learning, uncertainty quantification, and anomaly detection.

More broadly, the primary goal of my research is to lay information-theoretic foundations for the learning algorithms with limited labeled data, including transfer learning and semi-supervised learning, particularly with privacy, fairness, and/or communication constraints.