Ph.D. in Industrial Engineering, Georgia Institute of Technology (2020-2025)

B.S. in Pure Mathematics, The Chinese University of Hong Kong, Shenzhen (2016-2020)

Dr. Jie Wang received his Ph.D in Industrial Engineering at Georgia Institute of Technology in 2025. His research focuses on decision-making under uncertainty, through the lens of statistics and optimization, with practical applications in machine learning, healthcare, operations management, and wireless communication. His research has been published in several top journals and conferences such as Operations Research, Information and Inference: a Journal of the IMA, IEEE Journal on Selected Areas in Communications, IEEE Journal on Selected Areas in Information Theory, NeurIPS, ICML, and AISTATS. He has received several awards, such as Winner in the 2022 INFORMS Poster Competition, 2022 ISyE Robert Goodell Brown Research Excellence award, Winner of the Data Mining Best Theoretical Paper at 2023 INFORMS Workshop on Data Mining and Decision Analytics, Finalist of the INFORMS 2023 Data Mining Society's Data Competition, Runner-up of the INFORMS 2024 Data Mining Best Paper Award Competition, Runner-up of the INFORMS 2024 Computing Society Student Paper Award, Winner of the INFORMS 2024 Data Mining Society's Data Competition.

Journal articles

1. “Reliable Off-policy Evaluation for Reinforcement Learning”, Operations Research, 72(2): 699-716, 2024 (with R. Gao and H. Zha).

2. “A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks”, Information and Inference: A Journal of the IMA, 12(3): 1867-1897, 2023 (with M. Chen, T. Zhao, W. Liao and Y. Xie).

3. “On Achievable Rates of Line Networks with Generalized Batched Network Coding”, IEEE Journal on Selected Areas in Communications, vol. 42, no. 5, pp. 1316-1328, May 2024 (with S. Yang, Y. Dong and Y. Zhang).

4. J. Wang, R. Gao and Y. Xie. “Sinkhorn distributionally robust optimization”, Under Major Revision at Operations Research.

5. Y. Hu, J. Wang. X. Chen and N. He. “Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles”, Under Major Revision at a UTD Journal.

Conference articles

1. “Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances”, International Conference on Machine Learning, 2025 (with M. Boedihardjo, Y. Xie).

2. “Contextual stochastic bilevel optimization”, Advances in Neural Information Processing Systems, pp. 78412-78434, 2023 (with Y. Hu, D. Kuhn, A. Krause and Y. Xie).

3. “Improving Sepsis Prediction Model Generalization With Optimal Transport”, Machine Learning for Health, pp. 474-4885, 2022 (with R. Moore, Y. Xie and R. Kamaleswaran).

4. “Two-sample Test with Kernel Projected Wasserstein Distance”, International Conference on Artificial Intelligence and Statistics, pp. 8022-8055, 2022 (with R. Gao and Y. Xie).

5. “Two-sample Test using Projected Wasserstein Distance”, IEEE International Symposium on Information Theory, pp. 3320-3325 (with R. Gao and Y. Xie).

A complete list of publications can be found at https://walterbabyrudin.github.io/publication.html