Research Interests

Optimization, Artificial intelligence, Operations Research, Revenue Management

🔥 News

• September 2026: A New Paper FrontierOR: Benchmarking LLMs’ Capacity for Efficient Algorithm Design in Large-Scale Optimization is available on arXiv and has been accepted to the NeurIPS 2026 Evaluations and Datasets Track.
• August 2026: I have started my PhD in Operations Management in The University of Texas, at Dallas.
• January 2026: A New Paper TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction is available on arXiv.
• October 2025: I am actively seeking Ph.D. opportunities in Operations Research and Operations Management for Fall 2026!

💡 Publications

FrontierOR: Benchmarking LLMs’ Capacity for Efficient Algorithm Design in Large-Scale Optimization

Minwei Kong*, Chonghe Jiang*, Ao Qu, Wenbin Ouyang, Zhaoming Zeng, Xiaotong Guo, Zhekai Li, Junyi Li, Yi Fan, Xinshou Zheng, Xi Jing, Yikai Zhang, Zhiwei Liang, Seonghoo Kim, Runqing Yang, Zijian Zhou, Sirui Li, Han Zheng, Wangyang Ying, Ou Zheng, Chonghuan Wang, Jinglong Zhao, Hanzhang Qin, Cathy Wu, Paul Pu Liang, Jinhua Zhao, Hai Wang

• We introduce FrontierOR, among the first benchmarks to systematically evaluate LLM-based efficient algorithm design for realistic large-scale optimization problems.

TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction

Yuchen Fang*,Xinshou Zheng*, Javad Lavaei

• A stochastic trust-region method for unconstrained nonconvex optimization that incorporates stochastic variance-reduced gradients (SVRG) to accelerate convergence.

📖 Education

• August 2026 - present, The University of Texas at Dallas, Naveen Jindal School of Management.
      Ph.D. in Operations Management
• September 2023 - January 2025, Boston University, Questrom School of Business.
      M.Sc. in Mathematical Finance & Financial Technology
• June 2020 - August 2023, University of California, Santa Barbara, Department of Mathematics.
      B.Sc. in Applied Mathematics, Minor in Statistical Science

📂 Projects

• Classification of Cancer Tissues by Genes Based on Kolmogorov–Arnold Networks
  A PCA–KAN pipeline for cancer tissue classification from high-dimensional gene expression data.

• Summary of Stochastic Optimization
  A literature review on stochastic optimization methods, covering convergence properties, variance reduction, and practical trade-offs in large-scale and nonconvex settings.

🎖 Honors and Awards

• March 2024 Regional Finalist & Team Captain, 2024 PRMIA Risk Management Challenge.
• March 2023 Dean’s Honors, University of California, Santa Barbara. Winter 2023 quarter.
• June 2021 Dean’s Honors, University of California, Santa Barbara. Spring 2021 quarter.
• December 2020 Dean’s Honors, University of California, Santa Barbara. Fall 2020 quarter.

🛠 Skills

• Programming Languages:
  Python, R, C++, MATLAB, SQL

• Languages:
  Mandarin (Native), Hokkien/Taiwanese (Fluent), English (Fluent), German (Intermediate), Cantonese (Intermediate)

💬 Notes

• I have been fortunate to collaborate with researchers and scholars across multiple institutions. Please feel free to reach out if you are interested in related research directions!