• Multimetric Optimization (SigOpt Documentation)
• Metric Constraint (SigOpt Documentation)
• XGBoost Integration (SigOpt Documentation)
• sigopt-server, an open-source version of SigOpt
• libsigopt, the compuational engine of SigOpt

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.

• Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization, Materials Horizon (2019)
• Discovering high-performance broadband and broad angle antireflection surfaces by machine learning, Optica (2020)
• Beyond the Pareto efficient frontier: constraint active search for multiobjective experimental design, ICML (2021)
• Achieving Diversity in Objective Space for Sample-Efficient Search of Multiobjective Optimization Problems, Winter Simulation Conference (2022)

sequential design for materials science

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 multiobjective Bayesian optimization to discover and study the Pareto optimal anti-reflective nanostructures through numerical simulations.

• Co-optimizing battery storage for the frequency regulation and energy arbitrage using multi-scale dynamic programming, IEEE Transactions on Smart Grid (2016)
• Low-rank value function approximation for co-optimization of battery storage, IEEE Transactions on Smart Grid (2017)
• Code

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.