Research

A list of my working and published papers; for selected papers grouped by research topic, please see here. In the following, ^* means that authors are listed in alphabetical order. ⁺ means that authors are equally contributed.

Working Papers

• Translating Time-Series Foundation Models into AI-Enhanced Medication Inventory Control in a Large Urban Health System [Poster]

  D. Long, T. Wang, C. Branas, H. Lam, J. Sethuraman, D. U. Apakama, S. McCuskee, V. Mulchandani et al.

  Preliminary version appeared in the Main Track of the NeurIPS 2026 GenAI4Health Workshop.

• Pretrain-Then-Calibrate: Enhancing Optimality-Feasibility Tradeoff in Safe Contextual Optimization

  H. Lan, T. Wang, H. Lam

  Preliminary version appeared in the NeurIPS 2026 OPT Workshop on Optimization for Machine Learning.

• Achieving First-Order Statistical Improvements in Data-Driven Optimization: From No-Free-Lunch to Amplified Decision Perturbation

  H. Lam^*, T. Wang^*

  Preliminary version appeared in NeurIPS 2025 OPT Workshop on Optimization for Machine Learning.

  • Finalist, INFORMS George Nicholson Student Paper Competition (Winner TBA), 2026
  • Finalist, INFORMS Applied Probability Society Best Student Paper Award (Winner TBA), 2026
  • Finalist, INFORMS Data Mining Society Best Student Paper Award (declined), 2026

• Optimizing Pharmaceutical Control with Multi-Task Contextual Bandits: Addressing Batch Heterogeneity for Improved Manufacturing Efficiency

  T. Wang, N. P. Taskiran, and G. Iyengar

  Major revision at Manufacturing and Service Operations Management.

  • Finalist of MSOM Data-Driven Research Challenge 2025.

• DRO: A Python Library for Distributionally Robust Optimization in Machine Learning [Website] [Code] [Python package]

  T. Wang⁺, J. Liu⁺, H. Lam, H. Namkoong, J. Blanchet

  R&R (aka Major revision) at Journal of Machine Learning Research.

• Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework

  G. Iyengar^*, H. Lam^*, T. Wang^*

  Major revision at Management Science.

  Preliminary version appeared in AISTATS 2023.

• Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls

  T. Wang, N. Chen, and C. Wang

  Major revision at Manufacturing and Service Operations Management.

  Preliminary version: Distributionally Robust Prescriptive Analytics with Wasserstein Distance

Journal Articles (including minor revisions)

• Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization [Code] [Poster]

  G. Iyengar^*, H. Lam^*, T. Wang^*

  Minor revision at Management Science.

  • Honorable Mention of Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming 2025.

• Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data [Code]

  T. Wang⁺, J. Liu⁺, P. Cui, H. Namkoong

  Management Science, forthcoming.

  Preliminary version appeared in NeurIPS 2023.

• Data-Driven Distributionally Robust CVaR Portfolio Optimization Under A Regime-Switching Ambiguity Set [Code]

  C. S. Pun^*, T. Wang^*, Z. Yan^*

  Manufacturing and Service Operations Management, 25(5): 1779 - 1795, 2023.

Refereed Conference Publications

• A Hybrid Simulation-Machine Learning Approach to Improve EMS Responses in New York City

  D. Long, T. Wang, H. Lam, J. Sethuraman, M. Adams, M. Dadlani, K. Thomson, A. Turkcan et al.

  Winter Simulation Conference (WSC) 2026.

• Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM

  T. Wang, L. Zhang, R. Cummings

  International Conference on Artificial Intelligence and Statistics (AISTATS) 2026.

• Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? [Code] [Poster]

  G. Iyengar^*, H. Lam^*, T. Wang^*

  Advances in Neural Information Processing Systems (NeurIPS) 2024.

• Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications

  J. Liu, J. Wu, T. Wang, H. Zou, P. Cui

  International Conference on Machine Learning (ICML) 2024.

  Preliminary version appeared in NeurIPS 2023 Workshop on Distribution Shifts.

• On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets [Code] [Python package]

  J. Liu⁺, T. Wang⁺, P. Cui, H. Namkoong

  Advances in Neural Information Processing Systems (NeurIPS) 2023, Datasets and Benchmarks Track.

  • Highlighted as NeurIPS 2023 Favorite Papers by Two Sigma (9/3500+) [Link]

• Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation [Code]

  G. Iyengar^*, H. Lam^*, T. Wang^*

  International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.

  • Notable Paper (Oral presentation), 32/1689 = 1.9% of submissions