He completed his joint Ph.D. in Machine Learning and Statistics at Carnegie Mellon University, working with John Lafferty and Larry Wasserman. His research lies at the boundary of Modern Statistics and Computer Science. Especially he is interested in large-scale nonparametric methods, which directly conduct inference in infinite dimensional spaces and are more flexible to capture the subtleties in modern scientific applications. His long-term research goal is to develop a new generation of more powerful and principled statistical theories and machine learning algorithms to explore, understand, and predict large-scale, complex datasets.