I'm a PhD candidate in applied physics at Stanford University in Shanhui Fan's group, where I work on problems in nanophotonics, machine learning, and quantum optics. I graduated from the California Institute of Technology with a BS in physics and computer science in 2017.
Currently, I am an AI resident at Google X, working on an undisclosed project involving machine learning and large electromagnetic simulations.
My research focuses on optical computing architectures for classical and quantum information. Programmable on-chip interferometers can perform high-throughput and energy-efficient matrix computations and provide a promising hardware platform for machine learning. I have also worked on extending programmable optics to the quantum domain with an architecture for photonic quantum programmable gate arrays which can be reprogrammed to perform arbitrary quantum computations and have proposed a photonic quantum computer architecture which requires only a single controllable qubit.
Prior to coming to Stanford, I developed a popular quantum network simulation framework, wrote a functional testing system for camera subsystems on the Large Synoptic Survey Telescope, worked on vertex reconstruction algorithms for the Large Hadron Collider, and studied the evolution of the Earth's rotation.