I am a machine learning researcher at EY LLP and University of Maryland focused primarily on deep learning. My research approaches machine learning from an adversarial perspective. In particular, I’ve worked on poison attacks, adversarial examples, learning from noisy labels, and generalization phenomena.
Previously I worked for 8 years in optical physics, where I built laser-driven particle accelerators, ultrafast electron guns, ground- and space-based lidars, terahertz lasers, and frequency combs. I’ve worked at MIT, NASA, German Electron Synchrotron, and MIT Lincoln Lab.
My pivot to machine learning grew out of a fascination with the remarkable results of deep learning and curiosity about the inner workings of intelligence. I’ve since been on a mission to build robust, interpretable, and generalizable machines that can make sense of our complex, noisy, and (sometimes) adversarial world.
Ph.D., Electrical Engineering and Computer Science, 2017
Massachusetts Institute of Technology
B.Sc., Applied and Engineering Physics, 2009