Before joining NYU Stern, I did a postdoc with Professor Michael I. Jordan at the University of California, Berkeley. I earned my doctoral degree from the Machine Learning Department at the School of Computer Science at Carnegie Mellon University. My doctoral dissertation, entitled Learning with Sparsity: Structures, Optimization and Applications, was directed by the committee members: Dr. Jaime Carbonell, Dr. Tom Mitchell, Dr. Larry Wasserman, and Dr. Robert Tibshirani (from Stanford). I received the Simons-Berkeley Research Fellowship and IBM Ph.D. Fellowship. During my Ph.D., I did internships at Microsoft Research Redmond, IBM T.J. Watson Research Center and NEC Lab America.
Before that, I obtained my master of science in Industry Administration (Operations Research) from the ACO (algorithms, combinatorics and optimization) program in the Tepper School of Business at Carnegie Mellon. My master's work is advised by Prof. Manuel Blum.
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- Machine learning and Statistical Inference for high-dimensional and structured data analysis.
- Optimization methods and theory for large-scale and high-dimensional data analysis.
- Learning from collective intelligence: statistical learning and online decision making for crowdsourcing.
- Operations research/management problems, such as the optimal network design in process flexibility, approximate dynamic programming and revenue management.
- Applications to web text mining, recommendation systems, financial data anlaysis, environmental data analysis, and bioinformatics.