Ph.D. in Operations Research, Georgia Tech (2022 – 2024)

Ph.D. Candidate in Operations Research, Virginia Tech (2018 – 2022)

B.S. in Information Management & Information Systems, Shanghai University of Finance and Economics, China (2014 – 2018)

Dr. Yongchun Li received her Ph.D. in Operations Research from Georgia Tech in 2024. From 2024-2025, Dr. Li worked as an assistant professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville. Her research interests include optimization, machine learning (ML), and statistics, with the goal of advancing ML towards greater interpretability, efficiency, fairness, and robustness. Her research has received several recognitions, including Runner-up of the 2021 INFORMS Computing Society Student Paper Award, Winner of the 2020 INFORMS Data Mining Section Student Paper Award, and Winner of the 2019 Mixed Integer Programming Poster Competition at MIT.

1. Yongchun Li. The Augmented Factorization Bound for Maximum-Entropy Sampling. IPCO, 2025

2. Yongchun Li,  Weijun Xie. Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees. Operations Research, 2024

3. Yongchun Li,  Weijun Xie. Beyond Symmetry: Best Submatrix Selection for the Sparse Truncated SVD. Mathematical Programming, 2024

4. Yongchun Li,  Marcia Fampa, Jon Lee, Feng Qiu, Weijun Xie,  Rui Yao. D-optimal Data Fusion: Exact and Approximation Algorithms. INFORMS Journal on Computing, 2024

5. Yongchun Li,  Weijun Xie. Exact and Approximation Algorithms for Sparse PCA. INFORMS Journal on Computing, 2024

6. Yongchun Li, Santanu S. Dey, Weijun Xie. On Sparse Canonical Correlation Analysis. NeurIPS, 2024