Daniel Hsu
Associate Professor of Computer Science
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 PhD at UC San Diego and his BS 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 PhD 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.
Research Areas
- Applied and Theoretical Machine Learning
- Algorithmic Statistics
Additional information
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Professional Experience
- Associate Professor of Computer Science, Columbia University, 2018-
- Assistant Professor of Computer Science, Columbia University, 2013-2017
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Honors & Awards
- National Academy of Sciences Kavli Fellow, 2017
- Alfred P. Sloan Research Fellow in Computer Science, 2016
- IEEE Intelligent Systems “AI’s 10 to Watch”, 2016
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Education
- PhD, Computer Science, University of California, San Diego
- MS, Computer Science, University of California, San Diego
- BS, Computer Science and Engineering, University of California, Berkeley