Dr. Qiang Wu has research interests in mathematical foundations of machine learning, deep learning, data mining, and their applications. He has published more than 80 journal and conference papers and made contributions in multiple topics. In particular, he has developed fast and efficient algorithms for genetic association study, established mathematical foundations of information theoretic learning, advanced the theory of support vector machines and kernel methods, improved the divide and conquer approach for distributed machine learning, proposed new dimension reduction and feature selection methods, and applied newly developed machine learning, deep learning and statistical methods in the study of agroecosystems, stylometry analysis, biomedical research, and actuarial science. His research received support from NSF, Simons Foundations, USDA, etc.