Research by Topic

My research focuses on the design and deployment of AI-driven decision making in high-stakes environments. Selected papers in three directions (1) trustworthy decisions, (2) AI evaluation, and (3) healthcare operations are included below.

For the papers below, ^* indicates that authors are listed in alphabetical order. ⁺ indicates equal contributions.

Trustworthy decisions

I develop diagnostic tools and trustworthy methods across the data-to-decision pipeline, focusing on often-overlooked yet practically important components, including data acquisition, performance evaluation, and comparison.

• 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

• 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.

AI evaluation

I study systematic approaches to improving modern AI evaluation from an operations research perspective, including benchmark design and calibration.

• 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.

• 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.

• 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]

Healthcare operations

I am excited to collaborate with companies and government agencies to deploy AI-driven algorithms in healthcare settings.

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

  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.

• 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.

• 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.