Hello. I am a founding engineer at Distributional. Previously, I was the lead AI research scientist at SigOpt (acquired by Intel in 2020), where I worked on productionizing Bayesian optimization, and more broadly, sequential decision making problems. Prior to SigOpt, I obtained my Ph.D. in electrical engineering from Princeton University. My doctoral studies focused on approximate dynamic programming, stochastic optimization, and optimal learning, with an application in managing grid-level battery storage.

productionizing bayesian optimization

I spend most of my time at SigOpt developing and productionizing Baysian optimization software. It takes a great deal of engineering effort and care to ensure that the platorm is industry-grade, extensive, and robust under vastly different user demands. I have lead the development of many features such as multiobjective optimization, constrained optimization, model-aware optimization, as well as various backend computational improvements. The bulk of my work is now available as open source software sigopt-server and libsigopt.

sequential design for materials science

These projects are collaboration work with materials scientists from the University of Pittsburgh. At a high level, we frame materials design and discovery in the context of sequential decision making problems. In the first project, we develop a constrained Bayesian optimization method to accelerate the fabrication process of an optical device. In the second project, we use multiobjective Bayesian optimization to discover and study the Pareto optimal anti-reflective nanostructures through numerical simulations.

battery co-optimization

This project aims to co-optimize battery storage for multiple revenue streams. In particular, we are interested in the energy arbitrage and frequency regulation as the two main modes of operation. For the first time, we are able the model the problem down to the two-second resolution, which replicates the dynamics of the regulation signal. We also introduce the idea of low-rank value function approximation for backward dynamic programming.