My research

I work in the area of operations research within the ambit of applied mathematics. Broadly speaking, operations research is concerned with mathematically modeling various forms of uncertainty in engineered systems, and managing risks associated with these uncertainties. Within operations research, applied probability uses the theory of probability to model real world uncertainties and manage the concomitant risks.

My research interests as an applied probabilist encompasses methodological work in stochastic modeling, optimization and optimal control. I have a strong interest in applications of this methodology to machine learning, statistical inference and stochastic simulation of complex stochastic systems.

IAS fellowship

I propose to investigate the mathematical foundations of system identification (or model estimation) for spatio-temporally varying stochastic processes (such as density-dependent Markov processes) using free energy minimization. These foundational developments can potentially be useful for data-driven identification of emergent properties in complex systems using rare-event simulations. This problem lies at the intersection of the disciplines of applied probability, theoretical statistics and stochastic control, and I conjecture that tools from these disciplines will be necessary to develop these foundations.

My interests as an applied probabilist are broad, and I am passionate about using probabilistic tools to model, understand and control both natural and engineered systems. I look forward to the opportunity to interact with a broad range of IAS researchers.