Research

My research interests are diverse but linked through the common thread of computation-heavy statistical techniques, particularly Bayesian/semi-Bayesian techniques. My long-term research goals involve developing flexible statistical and machine learning methods with formal probabilistic guarantees for modern-day problems involving complex big and high-dimensional data, e.g., those arising in cancer genomics, computational biology, and medicine. Alongside methodology development, I also work on developing theoretical foundations and software implementations of novel statistical techniques. A few of my current (PI) research directions are as follows: (a) statistical modeling in cancer genomics and computational biology, (b) statistical methods for medical product safety assessment using real-world evidence, (c)) theoretical and methodological analysis of Bayesian methods and computations, and (d) statistical software development. I also find collaborative translational team-science research very rewarding and stimulating, and have participated as a statistical advisor or analyst in several collaborative research projects.

Please see Publications for a detailed list of publications. Feel free to send me an email if you’re interested in any of these directions and would like to discuss research and collaboration opportunities.