My aspirations to advance sustainable energy using computational materials research emerged from the convergence of pretty disparate influences. I first fell in love with materials science when I saw how different ceramic firing techniques affect the finish on pottery. Born and raised in the California Bay Area, I'd also grown to appreciate the capacity for computer science to augment scientific research, while simultaneously witnessing the detrimental effects of wildfires on people's lives. As an undergraduate at Stanford University, I've had the opportunity to explore broad intersections between all of these interests. My computational materials science research has focused on predicting local crystal structure around simulated particles. In the field of AI for climate change, I've performed research on automated dataset creation to facilitate urban transportation planning. At the University of Cambridge, I will combine what I've learned from these experiences by using generative machine learning models to accelerate materials discovery for batteries. Through this work during my MPhil and PhD, I hope to aid in designing more efficient renewable energy storage.
Stanford University Computer Science, AI Track 2021