My research interests span several areas such as privacy-preserving data analysis, machine learning, security, and information and coding theory. I am broadly interested in studying the tension/harmony between data analysis and machine learning on one hand and other notions of central importance to people and society such as privacy and security. In my work, I enjoy applying various tools from several areas such as information and coding theory, statistics, and optimization.

My research focuses on tackling current challenges in data analysis and machine learning especially those of direct impact on society. Much of my recent research effort has been devoted to developing practical algorithms with rigorous guarantees for privacy-preserving data analysis. The goal of this area of research is to enable conducting highly accurate analyses over private, personal data while providing rigorous guarantees of privacy for individuals whose data are collected; that is, to achieve the seemingly paradoxical goal of learning from private data without learning private data! Part of my research also addresses fundamental questions in machine learning and privacy.