My research interests mainly focus on the intersections between Theoretical Computer Science and Machine Learning. In particular, I am interested in the theoretical foundations of practical learning problems and the design of algorithms with rigorous guarantees therein. Topics under this umbrella include learning in sublinear models, learning with uncertainty and partial information, and machine learning applications with provable guarantees. More broadly, I am also interested in streaming algorithms and lower bounds, graph algorithms, statistical learning theory, and data processing privacy.