Daniel J. Hsu

Daniel Hsu is an associate professor in the Computer Science Department and a member of the Data Science Institute, both at Columbia University. Previously, he was a postdoc at Microsoft Research New England, and the Departments of Statistics at Rutgers University and the University of Pennsylvania. He holds a Ph.D. in Computer Science from UC San Diego, and a B.S. in Computer Science and Engineering from UC Berkeley. He was selected by IEEE Intelligent Systems as one of “AI’s 10 to Watch” in 2015 and received a 2016 Sloan Research Fellowship.

Daniel’s research interests are in algorithmic statistics and machine learning. His work has produced the first computationally efficient algorithms for several statistical estimation tasks (including many involving latent variable models such as mixture models, hidden Markov models, and topic models), provided new algorithmic frameworks for solving interactive machine learning problems, and led to the creation of scalable tools for machine learning applications.

His Ph.D. advisor at UCSD was Sanjoy Dasgupta. His postdoctoral stints were with Sham Kakade (at Penn) and Tong Zhang (at Rutgers).

(He does not usually refer to himself in the third-person.)