[CS] Fengxue Zhang Dissertation Defense/Sep 23, 2025

[via cs]

This is an announcement of Fengxue Zhang's Dissertation Defense.
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Candidate: Fengxue Zhang

Date: Tuesday, September 23, 2025

Time:  1 pm CST

Remote Location: https://uchicago.zoom.us/j/95891437634?pwd=cqOatif7qJBlsVPNwqNmP1gLVDsMLI.1

Location: JCL 390 

Title: Learning for Efficient, Scalable, and Constrained Bayesian Optimization in Real-World Applications

Abstract: Bayesian Optimization (BO) has emerged as a powerful framework for optimizing black-box functions where evaluations are expensive. However, deploying BO in complex real-world scenarios presents significant challenges, including high-dimensional search spaces, the presence of unknown constraints, the need to balance multiple objectives, and the demand for efficient end-to-end modeling and decision-making. This proposal outlines a research agenda focused on developing novel BO methodologies that are efficient, scalable, and capable of handling constraints to address these challenges. The proposed work is structured around three main thrusts: (1) Efficient Bayesian Optimization via Regions of Interest (ROI): We explore methods to learn ROIs to make BO more efficient, particularly in high-dimensional or heterogeneous applications. This involves adaptive level-set estimation to identify promising sub-regions of the search space. (2) Bayesian Optimization with Unknown Constraints: We develop principled approaches for BO problems where constraints are unknown and must be learned concurrently with the objective function. This includes COBAR for single-objective constrained BO, focusing on the principled treatment of feasibility, and CMOBO for constrained multi-objective BO, enabling a principled tradeoff among multiple objectives under learned constraints. (3) Efficient End-to-End Modeling and Decision Making (DRO): We investigate an end-to-end learning framework that moves beyond hand-crafted acquisition functions and myopic decision-making. This involves leveraging Decision Transformers for direct regret optimization, trained with a combination of simulated and real-world data. These research thrusts aim to significantly advance the capabilities of BO, enabling its application to a wider range of challenging real-world problems such as protein design, material discovery, and drug development.

Advisors: Yuxin Chen

Committee Members: Yuxin Chen, Rebecca Willett, Thomas Anthony Desautels, and Haifeng Xu