Education

Physical Sciences Division, The University of Chicago (Sep. 2021 - Oct. 2025 Expected)
Ph.D. student in Computer Science, GPA: 3.98
Focus: Machine Learning for Science, Bayesian Deep Learning, Bayesian Optimization (BO)

Physical Sciences Division, The University of Chicago (Sep. 2019 - Dec. 2020)
M.S. in Computer Science, GPA: 3.98
Focus: Full Stack Development, Machine Learning, High-Performance Computation

School of Data and Computer Science, Sun Yat-Sen University (Aug. 2015 - June. 2019)
B.S. in Information and Computing Science, GPA: 4.0/4.0, Ranking: 2/58
Focus: Machine Learning, High-Performance Computer

Honors & Services

  • Reviewer for Top Machine Learning Conferences:
    • NeurIPS 2023 (Top reviewer)
    • ICML 2022-2024
    • ICLR 2023-2024
    • IJCAI 2024
  • Scholarship:
    • China National Scholarship 2017-2018 (1/55)
    • Sun Yat-Sen Fellowship 2016-2019 (5/55)
    • Crerar Fellowship 2021-2022

Research Interests

My research interests center on learning and decision-making under conditions of uncertainty. Specifically, I am intrigued by the application of novel deep learning and statistical learning methods to diverse fields, including protein design, materials science, content distribution, and in-context learning for large language models. By performing uncertainty quantification on modern models, I aim to make sample-efficient decisions that proactively gather new data and propose incoming candidates or content, thereby optimizing application-specific objectives.

Publications

  1. (𝛼 − 𝛽) Han, Minbiao, Fengxue Zhang, and Yuxin Chen. “No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes.” In The 40th Conference on Uncertainty in Artificial Intelligence, 2024.

  2. Zhang, Fengxue, Jialin Song, James C. Bowden, Alexander Ladd, Yisong Yue, Thomas Desautels, and Yuxin Chen. “Learning regions of interest for Bayesian optimization with adaptive level-set estimation.” In International Conference on Machine Learning, pp. 41579-41595. PMLR, 2023.

  3. Zhang, Fengxue, Thomas Desautels, and Yuxin Chen. “Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition.” In the 28th International Conference on Artificial Intelligence and Statistics.

  4. Li, Diantong, Fengxue Zhang, Chong Liu, and Yuxin Chen. “ Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation.” In the 28th International Conference on Artificial Intelligence and Statistics.

  5. Zhang, Fengxue, Zejie Zhu, and Yuxin Chen. “Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimization.” In NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty.

  6. Zhang, Fengxue, Yair Altas, Louise Fan, Kaustubh Vinchure, Brian Nord, and Yuxin Chen. “Design of Physical Experiments via Collision-free Latent Space Optimization.” In NeurIPS 2020 Workshop on Machine Learning and the Physical Sciences.

Selected Projects

In-context-learning of LLM through Bayesian Optimization

University of Chicago, Sep. 2023 - Present

  • Developed the fixed set selection category by modeling the LLM and its in-context examples as a black-box model
  • Applied Bayesian Optimization (BO) to select the best set of permutations for different task types
  • Applied random embedding, sparse kernel, and trust-region identification techniques to overcome the curse of high dimensionality

Phosphate Sensor Calibration with Deep Learning

University of Chicago & Argonne National Lab, Sep. 2023 - Oct. 2024

  • Developed a novel preprocessing pipeline to calibrate cross-device variation together with variable selection and robust scaling
  • Developed a two-branch neural network composed of both 1D and 2D convolutional neural to fuse multiple information sources
  • Identified the optimized response window and predicted the true concentration of Phosphate in fertilizer solutions

Big Data Equity Market Analysis System

University of Chicago, Sep. 2020 - Dec. 2020

  • Implemented the big data system using Lambda architecture, with a self-implemented ZeroMQ-style message queue and front-end
  • Implemented distributed storage of the information in Google File System and optimized the Sharpe Ratio of the portfolio using Spark
  • Fine-tuned and distilled the Large Language Models using Information Bottleneck to process information

Distributed Crawling System

Sun Yat-Sen University, May. 2017 - Sep. 2017

  • Developed the crawling system based on the Scrapy-Redis framework
  • Employed Bloom Filter to improve storage and communication
  • Won the 3rd prize in the China Software Cup Software Design Competition 2017

Skills

  • Domain: (Large-scale) Machine Learning, Big data (Lambda architecture), Distributed System
  • Languages: Python, C/C++, MATLAB, Shell Script, Java, SQL, Go
  • Frameworks/Tools: Pytorch, BoTorch, TensorFlow, MATLAB Neural Network Toolbox, Git