Rimple Sandhu is a computational scientist with nine years of experience developing physics-aware and uncertainty-aware machine learning for energy, transportation, and ecological applications at a U.S. Department of Energy research lab. She blends a strong academic foundation—a PhD and MASc in Civil Engineering—with postdoctoral research at NREL to advance energy-efficient AI approaches like reservoir and brain-inspired computing that prioritize low-data, low-compute solutions. Her work spans scientific ML, Bayesian inference, and uncertainty quantification, bringing rigorous probabilistic modeling to real-world renewable energy problems. Based in Boulder, Colorado, she is drawn to interdisciplinary projects that translate physical principles into robust, resource-conscious ML systems.
9 years of coding experience
6 years of employment as a software developer
Indian Institute of Technology Bombay
Doctor of Philosophy - PhD, Civil Engineering, Doctor of Philosophy - PhD, Civil Engineering at Carleton University
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