I am a quantitative researcher at a proprietary trading firm, based in Chicago and intermittently in New York City. Previously, I completed my bachelor's and master's degrees at Stanford University, under the guidance of Professor Stephen Boyd. I was a Neo Scholar1, and have spent time at Jump Trading, Squarepoint Capital, Bridgewater Associates, and Amazon.
My research philosophy favors ideas that are simple and capable of scale. Currently, I investigate deep learning solutions for problems in financial markets, and have previously worked on convex optimization, statistical learning, and applied modern machine learning [healthcare, social science]. I recently gave a talk at NeurIPS. Particular themes that excite me today are: differentiable optimization, long sequence modeling, generative model-augmented statistics, and decision making under uncertainty.
At Stanford, I was heavily involved in teaching. I was a TA twice for Math 104: Applied Matrix Theory and twice for the famous EE 364A: Convex Optimization2.
Outside of work and research, I enjoy golf, table tennis, learning about painting3 and horology, and card games.
Interested in any of the above, or just want to chat? Please reach out -- my email is firstlast [at] alumni [dot] stanford [dot] edu.