Summary
Kenny Chowdhary is a Senior Data Scientist with over a decade of experience bridging machine learning, Bayesian inference, and uncertainty quantification for large-scale scientific and engineering models. He has driven prediction, calibration, and bias-correction work for exascale climate and hypersonics applications at Sandia and now applies those skills at NVIDIA, combining geometric MCMC, Gaussian processes, and deep learning to tackle high-dimensional, multi-target regression and model-error quantification. Kenny specializes in building robust ML workflows and parallel model-query pipelines—often leveraging scikit-learn APIs—for scalable hyperparameter tuning, feature selection, and Bayesian optimization. His academic foundation (PhD in Applied Mathematics from Brown) underpins practical advances in calibration under uncertainty, including use of relative-entropy risk bounds and function registration for complex distributions. A less obvious strength is his fluency across numerics, probabilistic theory, and software engineering, enabling him to translate theoretical UQ techniques into production-ready tools for climate-scale simulations.
10 years of coding experience
3 years of employment as a software developer
PhD, Applied Mathematics, PhD, Applied Mathematics at Brown University
BA, Mathematics, BA, Mathematics at New York University