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Nisha Chandramoorthy is an Assistant Professor at the University of Chicago in the Department of Statistics. Her research interests lie at the intersection of applied dynamical systems, ergodic theory, scientific computing, and computational statistics. She has previously held the position of Early-Career Assistant Professor at the School of Computational Science and Engineering at Georgia Tech and completed her postdoctoral work at MIT in the Institute for Data, Systems, and Society. Nisha obtained her PhD and Master's degrees from MIT and her B. Tech. from IIT Roorkee. Her current focus is on developing sampling algorithms for Bayesian posteriors, with an aim to enhance and accelerate inference in dynamical systems. Nisha is particularly interested in using dynamical systems approaches to optimize and learn algorithms that operate on dynamic operators. She aims to generalize existing results and create effective new algorithms, which include analyses and developments targeting computational engineering tasks such as sensitivity analysis, model selection, Bayesian inference, data assimilation, and model order reduction in the context of partial differential equation models, especially those relevant to geosciences.
Department of Philosophy