Sunash Sharma is a PhD candidate in EECS at UC Berkeley and a Graduate Research Aide at Argonne National Laboratory focused on optimization strategies for power systems under deep uncertainty and large-scale grid capacity expansion. With a Caltech background in applied physics and computer science and membership in the inaugural Schmidt Academy, he blends rigorous modeling, software engineering, and reproducible research to build simulation platforms for EV charging and algorithms for optimal battery dispatch. His work spans theory-to-practice—from integrating grid reliability assessments with capacity planning to implementing Python simulations in fluid dynamics and quantum annealing experiments—reflecting a decade of interdisciplinary research experience. Sunash is seeking industry roles starting Fall 2026 and brings uncommon cross-domain fluency that helps translate advanced optimization methods into deployable tools for energy systems.
10 years of coding experience
Doctor of Philosophy - PhD, EECS, Doctor of Philosophy - PhD, EECS at University of California, Berkeley
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