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Interactive learning - (Daniel Hsu)
Daniel Hsu is an assistant professor in the Department of Computer Science 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 received a 2014 Yahoo ACE Award, 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, machine learning, and privacy. His work has produced the first computationally efficient algorithms for numerous statistical estimation tasks (including many involving latent variable models such as mixture models, hidden Markov models, and topic models), provided new algorithmic frameworks for tackling interactive machine learning problems, and led to the creation of highly-scalable tools for machine learning applications.
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