Hello. I am a research engineer at SigOpt. I obtained my Ph.D. in electrical engineering from Princeton University, where I was advised by Prof. Warren B. Powell. My doctoral studies focused on approximate dynamic programming, stochastic optimization, and optimal learning, with an application in managing grid-level battery storage. Currently, I work on productionizing Bayesian optimization, and more broadly, sequential decision making problems.
These projects are collaboration work with materials scientists from the University of Pittsburgh. At the 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 multi-objective Bayesian optimization to discover and study the Pareto-optimal anti-reflective nanostructures in numerical simulations.
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.
This project implements an optimal learning algorithm with a locally parametric belief model. This approach is motivated by the need to balance parametric models (that make assumption on the function structure but are efficient in high dimensions) and nonparametric look-up table models (that make no assumption on the function structure but are highly susceptible to the curse of dimensionality).