Sharma, Upanshu

Dr Upanshu Sharma is a lecturer in the School of Mathematics and Statistics at The University of New South Wales. His research focuses on partial differential equations, probability theory, and computational statistical mechanics, with an emphasis on coarse-graining of stochastic dynamics, large deviations, and sampling algorithms for molecular dynamics. He has held positions as a Humboldt Research Fellow at FU Berlin and CERMICS (École des Ponts ParisTech) and has contributed to various publications in leading journals. His work explores connections between variational structures and evolution equations arising from stochastic particle systems.

Upanshu Sharma is a researcher at UNSW Sydney, focusing on mathematical and computational methods for analyzing complex stochastic systems. His work explores the quantification of coarse-graining errors in Langevin dynamics, variational approaches to coarse-graining generalized gradient flows, and non-reversible stochastic differential equations. He investigates effective dynamics, non-reversible sampling schemes, and connections between coarse-graining and averaging. His research also includes stochastic gradient descent, non-equilibrium functional inequalities, and the analysis of wavelet transforms on the similitude group. Sharma's contributions span both theoretical and applied aspects of stochastic processes, with an emphasis on understanding fluctuations, entropy distances, and large deviations in non-equilibrium systems.