StatML@Duke

Machine learning and Bayesian statistics. Discovering the latent structure in data.

Neural coding and computation, Bayesian behavioral modeling, and statistical analysis of neural data.

Machine learning applications in genetics, genomics and other biologcial areas.

Bayesian statistical models for medicine, Bayesian nonparametrics and scalable inference.

Modeling human behaviors and dynamics. Developing fast inference algorithms.

Understanding brains, including neuronal recording and functional imaging data.

Estimation and model selection under both "large p, small n" and "large n, small p" settings, and many more.

Developing clustering methodology and transfer learning techniques for healthcare data.

My research interests are machine learning and staochastic differential equations.