My principal research interests lie in the development of efficient algorithms and intelligent systems which can learn from a massive volume of complex (high dimensional, nonlinear, multi-modal, skewed, and structured) data arising from both artificial and natural systems, reveal trends and patterns too subtle for humans to detect, and automate decision making processes in uncertain and dynamic possible world. I develop core machine learning methodology, including kernel methods, feature space embedding methods, graphical models, probabilistic and stochastic modeling, scalable algorithms, optimization algorithms and deep learning models.

I am also interested in developing machine learning models and algorithms to address interdisciplinary problems. For instance, I've conducted research on the management of information diffusion networks and recommendation systems, the discovery of time-varying gene regulatory networks, the understanding of disease progression, the extraction of topics based on online document feeds, the prediction of materials properties, and the predictive modeling of robotic systems.