(6bm) Optimal Control of Neural and Small Length Scale Dynamical Systems | AIChE

(6bm) Optimal Control of Neural and Small Length Scale Dynamical Systems


In the last decade, dramatic advancements in experimental and computational techniques have flushed tremendous opportunities in the study of fundamental questions of science and engineering by taking the approach of stochastic modeling and control of dynamical systems. In parallel, emerging applications through these advances have ignited the development of new technologies by integrating advanced control strategies with stochastic dynamical models. One such example is the brain-machine interface. Over the course of my doctoral and postdoctoral studies, my research interests have mainly been focused on developing optimal control strategies for applications in closed-loop neural prostheses, deep brain stimulations, and closed-loop drug delivery systems. During this poster session, I will highlight examples from both my graduate research and postdoctoral research which demonstrate my capability in advancing these subjects by integrating probabilistic tools with optimal control policies. Building on these results, I will describe my future academic plans to commence a distinct, interdisciplinary and independent research program at the interface of engineering and computational neuroscience. I will explain how my interdisciplinary expertise in the theory of stochastic processes, neuroscience, optimization, dynamical systems and control uniquely positions me to capitalize on emerging opportunities at this area.

I am currently working as a postdoctoral research associate in the department of Electrical and Systems Engineering at Washington University in St. Louis. The major theme of my studies is centered on understanding the effect of system dynamics on the emerged system behaviors such as synchronization in complex dynamical systems, in particular pathological synchronization in Parkinson's disease, and developing optimal control strategies to achieve the desired system behavior in such systems. During the course of my doctoral studies at Lehigh University, I developed optimal control-theoretic framework for investigating the design of closed-loop neural prostheses. In parallel, I developed optimal stochastic control strategies for regulating behaviors of particles driven by Brownian motion.