281894 Stochastic Modeling and Control of Neural and Small Length Scale Dynamical Systems

Sunday, October 28, 2012
Hall B (Convention Center )
Gautam Kumar, Chemical Engineering, Lehigh University, Bethlehem, PA

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 studies, my research interests have mainly been focused on developing optimal control strategies for applications in closed-loop neural prostheses and small length scale stochastic systems using a purely probabilistic framework. During this poster session, I will highlight examples from my graduate research which demonstrate my capability in advancing these subjects by integrating probabilistic tools with advanced 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, nonlinear dynamical systems and process control uniquely positions me to capitalize on emerging opportunities at this area.

I am currently pursuing doctoral studies in Chemical Engineering at Lehigh University. The major theme of my studies is centered on developing control-theoretic framework for investigating the role of sensory feedback in closed-loop neural prostheses. A direct impact of such study can lead to a clinical deployment of neural prostheses in rehabilitation. In parallel, I have developed optimal stochastic control strategies for regulating behaviors of particles driven by Brownian motion. Emerging applications from this study include but are not limited to 1) understanding the dynamical behaviors of biological objects such as intra-cellular and DNA interactions in their natural environments for facilitating biomedical applications, 2) manipulation and regulation of nano and molecular structures for applications in drug delivery systems and laboratory on a chip technology.

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