Publications and Preprints

My research interests lie broadly in data-driven stochastic optimization, with applications to trustworthy machine learning, supply chain management, and continuous manufacturing. My recent research focuses on understanding the performance of data-driven optimization algorithms and their limits under heterogeneous data. This includes developing:

theoretical tools in decision evaluation and selection [W3, J3, C4];
empirical testbed and data-driven tools in understanding distribution shifts [W2, J5, J4].

Practically, I have been working with several companies and institutions (FDNY, MSD) to help design and implement data-driven optimization models in real operations. In Summer 2023, I spent a great time working on uncertainty attribution of inventory production control systems as a research scientist intern at Amazon.

For papers listed as follows, ^* means that authors are listed in alphabetical order. ⁺ means that authors are equally contributed (order listed alphabetically).

Working Paper

[W3] Achieving First-order Statistical Improvements in Data-Driven Optimization
Henry Lam, Tianyu Wang ^*
[W2] DRO: A Python Library for Distributionally Robust Optimization in Machine Learning [Website][Code]
Jiashuo Liu⁺, Tianyu Wang⁺, Henry Lam, Hongseok Namkoong, Jose Blanchet
[W1] Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls
Tianyu Wang, Ningyuan Chen, and Chun Wang

Preliminary version: Distributionally Robust Prescriptive Analytics with Wasserstein Distance

Journal Publications and Revisions

[J5] Optimizing Pharmaceutical Control with Multi-Task Contextual Bandits: Addressing Batch Heterogeneity for Improved Manufacturing Efficiency.
Tianyu Wang, Naz Pinar Taskiran, and Garud Iyengar
Finalist of MSOM Data-Driven Research Challenge 2025 (Winner TBD).
[J4] Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data
Jiashuo Liu⁺, Tianyu Wang⁺, Peng Cui, Hongseok Namkoong
Major revision at Management Science.
Preliminary version appeared in NeurIPS 2023 [C2].
[J3] Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Garud Iyengar, Henry Lam, Tianyu Wang^*
Major revision at Management Science.
Finalist of Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming 2025 (Winner TBD).
[J2] Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework
Garud Iyengar, Henry Lam, Tianyu Wang^*
Major revision at Management Science.
Preliminary version appeared in AISTATS 2023 [C1].
[J1] Data-Driven Distributionally Robust CVaR Portfolio Optimization Under A Regime-Switching Ambiguity Set [Code]
Chi Seng Pun, Tianyu Wang, Zhenzhen Yan^*
Manufacturing and Service Operations Management, 25(5): 1779 - 1795, 2023.

Refereed Conference Publications

[C4] Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?
Garud Iyengar, Henry Lam, Tianyu Wang^*

Advances in Neural Information Processing Systems (NeurIPS) 2024.
[C3] Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Peng Cui
International Conference on Machine Learning (ICML), 2024.
Short version appeared in NeurIPS 2023 Workshop on Distribution Shifts.
[C2] On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets [Code] [Python package]
Jiashuo Liu⁺, Tianyu Wang⁺, Peng Cui, Hongseok Namkoong
Advances in Neural Information Processing Systems (NeurIPS) 2023, Datasets and Benchmarks Track.
Highlighted as NeurIPS 2023 Favorite Papers by Two Sigma (9/3500+)
[C1] Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation [Code]
Garud Iyengar, Henry Lam, Tianyu Wang^*
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
Notable Paper (Oral presentation), 32/1689 = 1.9% of submissions