I’m a 4th year PhD Candidate in Chemical Engineering at University of Wisconsin-Madison advised by Dr. Joel Paulson. My research interest lies in Bayesian Optimization, Gaussian processes, decision-making under uncertainty, deterministic global optimization, and Process System Engineering.

I received my BS and MS in Chemical Engineering at National Taiwan University, where I was advised by Dr. Jeffrey Ward.

Bulletin

• Our paper AI-Driven Scenario Discovery: Diffusion Models and Multi-Armed Bandits for Building Control Validation has been published at the Energy and Buildings journal.
• Wei-Ting has started a new position as a 2025 ADISE research intern in the Machine Learning, Optimization, and Statistics (MiLOS) team at the Dow Chemical Company.
• Out paper CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization has been selected as the winner for the IEEE CSS TC Process Control Outstanding Student Paper Prize.
• Our paper TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions has been published at the NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty.
• Our poster BEACON-A Bayesian Novelty Search Algorithm for Efficient Material Property Exploration has been awarded as the 1st place poster presentation at the 13th Graduate Research Symposium at the Ohio State University CBE department.
• Our paper Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks has been published at the 2024 IEEE Conference on Control Technology and Applications (CCTA).
• Our work Scalable Global Optimization of Gaussian Processes Using a Specialized Branch-and-Bound Algorithm has been selected to present at the CAST Division Planery session at the 2024 AIChE annual meeting.
• Our preprint BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems is now available on arxiv.