I have broad interests across the computing stack, spanning hardware design to algorithms. My research is broadly concerned with machine learning applied to computational engineering, with previous work on generative modeling for CAD and molecule design, asynchronous graph neural networks for swarm robotics, a differentiable Monte Carlo variant for computational geometry and topology optimization, and inverse design for optical neural networks. Before coming to Princeton, I did my BS in mathematics at Harvey Mudd College.