Daniel Hsu is an associate professor in the Department of Computer Science and a member of the Data Science Institute, both at Columbia University. He works on algorithmic statistics and machine learning, with the goals of designing efficient algorithms for learning and data analysis, and understanding the limits of efficient computation for these tasks. Daniel completed his Ph.D. at UC San Diego and his B.S. at UC Berkeley. He was a postdoc at the Departments of Statistics at Rutgers University and the University of Pennsylvania and also at Microsoft Research New England. He was selected by IEEE Intelligent Systems as one of “AI’s 10 to Watch” in 2015 and received a Sloan Research Fellowship in 2016.
His Ph.D. advisor at UCSD was the glorious Sanjoy Dasgupta. His postdoctoral stints at Penn and Rutgers were with the equally glorious Sham Kakade and Tong Zhang.
