I am currently a doctoral candidate in the Machine Learning Department at the School of Computer Science at Carnegie Mellon University and fully expect to complete my Ph.D. degree in May 2013. My doctoral dissertation, entitled Learning with Sparsity: Structures, Optimization and Applications, is directed by the committee members: Dr. Jaime Carbonell (advisor), Dr. Tom Mitchell, Dr. Larry Wasserman, and Dr. Robert Tibshirani (from Stanford). I was awarded an IBM Ph.D. Fellowship for two consecutive academic years 2011~2013. Thanks IBM!
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.
- Deterministic and stochastic optimization for large-scale and high-dimensional data analysis.
- Machine learning and statistical methods for understanding and exploring hidden structures and relations from large-scale complex data.
- Applications in web text mining, recommendation systems, environmental data analysis, and bioinformatics.
- Learning from collective intelligence: building reliable and cost-saving crowdsourcing systems.