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Nisha Chandramoorthy is an Assistant Professor in the Department of Statistics at the University of Chicago, with a background in applied dynamical systems, ergodic theory, and scientific computing. Previously, she served as an Early-Career Assistant Professor at the School of Computational Science and Engineering at Georgia Tech and completed a postdoctoral fellowship at MIT's Institute for Data, Systems, and Society. Nisha received her Ph.D. and Master's degrees from MIT and holds a B.Tech. from IIT Roorkee. Her research primarily focuses on sampling algorithms for Bayesian posteriors, aiming to accelerate inference in dynamical systems. She has a robust interest in leveraging dynamical systems approaches for optimizing sampling and learning algorithms in dynamic operators. Her work includes the development of algorithms for computational engineering tasks such as sensitivity analysis, model selection, Bayesian inference, data assimilation, and model order reduction utilizing dynamical data and partial differential equation models, particularly in geosciences.
Department of Philosophy