I am a quantitative researcher at a proprietary trading firm, based in Chicago and intermittently in New York City. I obtained 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. I currently study deep learning approaches to problems in financial markets; with prior work across convex optimization, statistical learning, and applied modern machine learning [medicine, social sciences]. Recently, I gave a talk at NeurIPS. Some 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 play golf and table tennis, learn about painting3 and horology, read in macroeconomics, and muse on 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.