Ravi

Varadhan

,

PhD

My research interests lie in three widely different areas: (i) aging and frailty, (ii) patient-centered outcomes research, and (iii) computational statistics.   My work in aging and frailty is aimed at development of theoretical and statistical models for studying frailty as dysregulated homeostasis that increases the vulnerability to stressors.  In particular, the focus is on the markers of HPA axis, sympathetic nervous systems and inflammation dynamical systems biology in frailty.  Another major focus of mine is on developing statistical methods that inform evidence-based individualized medicine. I am interested in identifying factors that are responsible for the heterogeneity in how individuals respond to interventions, which is known as the heterogeneity of treatment effect (HTE). I am also developing a new statistical approach for cross-deign synthesis that applies sophisticated statistical methods to combine randomized, controlled trial and registry data to obtain valid evidence for groups such as elderly women, who are under-represented in the trials. Last, but not least, I am interested in developing algorithms and software for solving high-dimensional optimization problems arising in statistical modeling. I have developed a new class of algorithms called SQUAREM (squared extrapolation methods) for accelerating the convergence of expectation-maximization (EM) and minorization-maximization (MM) algorithms.  The EM/MM algorithms are popular approaches in statistical modeling for maximum likelihood estimation. SQUAREM algorithms increase the speed of EM/MM algorithms substantially without compromising the robustness. I have written several R packages for solving various types of problems in statistical modeling and in optimization.