avatar

Wei-Ting (Jonathan) Tang

Ph.D. Candidate in Chemical Engineering
University of Wisconsin-Madison
wtang98 (at) wisc.edu


About Me

I’m a 5th year Ph.D. Candidate in Chemical Engineering at the University of Wisconsin-Madison, advised by Dr. Joel Paulson. I develop sample-efficient methods for optimizing expensive black-box systems, spanning high-dimensional Bayesian optimization, Bayesian novelty search, and global optimization of Gaussian process models, with applications in material discovery, energy building system, and process system engineering.

Previously, I was a research intern in the Machine Learning, Optimization, and Statistics (MiLOS) group at Dow Chemical (2025) and received my B.S. and M.S. in Chemical Engineering from National Taiwan University, advised by Dr. Jeffrey Ward.

Research Interests

Selected Publications

  1. Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions arXiv
    Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions
    Wei-Ting Tang, M. Muthyala, Joel A. Paulson
    arXiv preprint, 2026.
    PDF

  2. DD
    BEACON: a Bayesian optimization inspired strategy for efficient novelty search
    Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson
    Digital Discovery, 2026.
    PDF

  3. OPTIMAL
    Rethinking Trust Region Bayesian Optimization in High Dimensions
    Wei-Ting Tang, Joel A. Paulson
    AISTATS 2026 Workshop (OPTIMAL), 2026.
    PDF

  4. arXiv
    An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
    Wei-Ting Tang, Akshay Kudva, Calvin Tsay, Joel A. Paulson
    arXiv preprint, 2026.
    PDF

  5. AISTATS
    NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
    Wei-Ting Tang, Akshay Kudva, Joel A. Paulson
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2026.
    PDF

  6. E&B
    AI-Driven Scenario Discovery: Diffusion Models and Multi-Armed Bandits for Building Control Validation
    Wei-Ting Tang, A. P. Vinod, F. Germain, Joel A. Paulson, C. R. Laughman, Ankush Chakrabarty
    Energy & Buildings, 116207, 2025.
    PDF

  7. CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization CDC
    CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization
    Wei-Ting Tang, Joel A. Paulson
    IEEE Conference on Decision and Control (CDC), 1547–1552, 2024.
    PDF IEEE CSS TC Process Control Outstanding Student Paper Prize

Services

Conference Reviewer

Journal Reviewer


Powered by Jekyll and Minimal Light theme.