Chen Wang

Welcome! I'm a postdoc researcher hosted jointly by Vladimir (Vova) Braverman at Rice University and Samson Zhou at Texas A&M University.

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.

Prior to my current role, I completed my Ph.D. (pending commencement) at Rutgers University, where I was extremely fortunate to be advised by  Sepehr Assadi. Even before, I received my MSc degree from University College London (UCL), England. I got my B.Eng. degree from Northwestern Polytechnical University, China. I also spent a semester at Shih Hsin University, Taiwan, and briefly worked at Sichuan University, China. Outside academia, I have research experience at Google Research, where I was an intern mentored by Rajesh Jayaram.