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Prospective students: Due to the volume of inquiries, I am unable to respond to individual emails about positions in my group. Admissions to our graduate program are managed at the department level. To apply, see here.

News

• April 2026: Our paper “Distributionally Robust Optimization with Decision-Dependent Information Discovery” has been accepted for publication in Mathematical Programming. [ISE News]

• April 2026: Congratulations to Hyuk Park on his appointment as Assistant Professor at Korea National Defense University!

• April 2026: I am honored to be recognized with the Facilitating Learning Excellence (FLEX) Award.

• February 2026: I presented our work “Data-Driven Contextual Optimization with Gaussian Mixtures” at the Banff Workshop on Contextual Stochastic Optimization. [video]

• January 2026: Our paper “DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty” has been accepted to ICLR 2026.

• January 2026: Our paper “On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya–Watson Approach” has been accepted for publication in Manufacturing & Service Operations Management!

• September 2025: Two of our papers, “Distributionally Robust Performative Optimization” and “Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification,” have been accepted to NeurIPS 2025!

• July 2025: Congratulations Hank Park for successfully defending his thesis on “Data-Driven Robust Solution Schemes for Sequential Decision Making”!

• January 2025: Our paper “Robust System Identification: Finite-Sample Guarantees and Connection to Regularization” has been accepted to ICLR 2025!

• October 2024: Our paper “Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes” has been accepted to NeurIPS 2024!

• September 2024: Congratulations to Yijie Wang on his appointment as Assistant Professor at the School of Economics and Management, Tongji University!

• August 2024: Thank you NSF for supporting our project on Distributionally Robust Quadratic Optimization for Power Systems Applications.

• May 2024: I will present our work “Distributionally Robust Path Integral Control” at the Robust Optimization Webinar [video].

• Apr 2024: Our paper “Distributionally Robust Observable Strategic Queues” has been accepted for publication in Stochastic Systems.

• Apr 2024: Congratulations Zhuangzhuang Jia for receiving the Ben Hamilton Graduate Research Award!

• Mar 2024: Our paper “Wasserstein Robust Classification with Fairness Constraints” has been accepted for publication in Manufacturing & Service Operations Management.

• Feb 2024: Our paper “Learning Fair Policies for Multi-Stage Selection Problems from Observational Data” has accepted for oral presentation at AAAI 24!

• Jan 2024: I have joined the editorial board of Operations Research as an Associate Editor for the Optimization area.

• Jan 2024: I am honored to be included in the List of Teachers Ranked as Excellent By Their Students.

• June 2023: Our paper “Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming” has been accepted for publication in Operations Research. A news article is available at ISE announcement.

• April 2023: Xiangyi Fan has successfully defended her thesis on “Distributionally Robust Approaches for Two-stage Optimization and Interdiction Problems Under Uncertainty.” Congratulations!

• March 2023: Congratulations Yijie Wang for successfully defending his thesis on “Robust Solution Schemes for Queue Management, Fair Classification, and Portfolio Selection Problems”!

• July 2022: Our paper on “A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers” has been accepted for publication in Operations Research.

Publications

Preprints

• Distributionally Robust Optimization via Targeted Integral Probability Metric for General Data Processes, with L. Fang, J. Cheng, and Y. Wang. Available online, 2026.

• Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions, with Y. Yoon and Y. Wang. Available online, 2025.

• A Distributionally Robust Optimization Approach to Quick Response Models under Demand Uncertainty, with P. Papavassilopoulos and Y. Wang. Available online, 2025.

• Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control, with Z. Jia, H. Park, and G. Dayanıklı. Available online, 2025.

• Sample Complexity of Data-driven Multistage Stochastic Programming under Markovian Uncertainty, with H. Park. Available online, 2024.

• Generalization Bounds for Contextual Stochastic Optimization using Kernel Regression, with Y. Wang and C. P. Ho. Available online, 2024.

• Distributionally Fair Stochastic Optimization using Wasserstein Distance, with Q. Ye and W. Xie. Available online, 2024.

• Data-Driven Stochastic Dual Dynamic Programming: Performance Guarantees and Regularization Schemes, with H. Park and Z. Jia. Available online, 2022.

• Robust Contextual Portfolio Optimization with Gaussian Mixture Models, with Y. Wang and C. P. Ho. Available online, 2022.

Journal Papers

• Distributionally Robust Optimization with Decision-Dependent Information Discovery, with Q. Jin, A. Georghiou, and P. Vayanos. Mathematical Programming, 2026.

• On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach, with Y. Wang, P. Srivastava, and C. P. Ho. Manufacturing & Service Operations Management, 2026.

• Improving Transportation Network Redundancy Under Uncertain Disruptions via Retrofitting Critical Components, with K. Qu, X. Fan, X. Xu, and A. Chen. Transportation Research Part B, 2025.

• Second-order Bounds for the M/M/s Queue with Random Arrival Rate, with W. van Eekelen, J. J. Hasenbein, and J. van Leeuwaarden. Queueing Systems, 2025.

• Discrete-Time Stochastic LQR via Path Integral Control and Its Sample Complexity Analysis, with A. Patil and T. Tanaka. IEEE Control Systems Letters, 2024.

• Distributionally Robust Observable Strategic Queues, with Y. Wang, M. N. Prasad, and J. J. Hasenbein. Stochastic Systems, 2024.

• Wasserstein Robust Classification with Fairness Constraints, with Y. Wang and V. A. Nguyen. Manufacturing & Service Operations Management, 2024.

• A Decision Rule Approach for Two-Stage Data-Driven Distributionally Robust Optimization Problems with Random Recourse, with X. Fan. INFORMS Journal on Computing, 2023. [code]

• Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming, with G. Xu. Operations Research, 2023. [code]

• Robust Control of Maximum Photolithography Overlay Error in a Pattern Layer, with N. Graff and D. Djurdjanovic. CIRP Annals, 2023.

• A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers, with P. Srivastava and P. Sarkar. Operations Research, 2022.

  • Honorable Mention at the INFORMS Computing Society Student Paper Competition

• Linearizing Bilinear Products of Shadow Prices and Dispatch Variables in Bilevel Problems for Optimal Power System Planning and Operations, with N. Laws. IEEE Transactions on Power Systems, 2022.

• Distributionally Robust Chance-Constrained Optimal Transmission Switching Problems, with Y. Zhou and H. Zhu. IEEE Transactions on Sustainable Energy, 2022.

• Finding Minimum Volume Circumscribing Ellipsoids Using Generalized Copositive Programming, with A. Mittal. Operations Research, 2021. [code]

• Optimal Residential Battery Storage Operations Using Robust Data-driven Dynamic Programming, with N. Zhang and B. D. Leibowicz. IEEE Transactions on Smart Grid, 2019.

• Robust Quadratic Programming with Mixed-Integer Uncertainty, with A. Mittal and C. Gokalp. INFORMS Journal on Computing, 2019.

• Improved Conic Reformulations for K-means Clustering, with M. N. Prasad. SIAM Journal on Optimization, 2018.

• Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls, with D. Kuhn. Operations Research, 2018. [code]

• Data-Driven Inverse Optimization with Imperfect Information, with P. Mohajerin Esfahani, S. Shafieezadeh-Abadeh and D. Kuhn. Mathematical Programming B, 2017.

• Ambiguous Joint Chance Constraints under Mean and Dispersion Information, with V. Roitch, D. Kuhn and W. Wiesemann. Operations Research, 2017.

• K-Adaptability in Two-Stage Distributionally Robust Binary Programming, with D. Kuhn and W. Wiesemann. Operations Research Letters, 2015.

• A Comment on “Computational Complexity of Stochastic Programming Problems”, with D. Kuhn and W. Wiesemann. Mathematical Programming A, 2015.

• K-Adaptability in Two-Stage Robust Binary Programming, with D. Kuhn and W. Wiesemann. Operations Research, 2015.

• A Distributionally Robust Perspective on Uncertainty Quantification and Chance Constrained Programming, with V. Roitch, D. Kuhn and W. Wiesemann. Mathematical Programming B, 2015. [Technical Report]

• Distributionally Robust Multi-Item Newsvendor Problems with Multi-Modal Demand Distributions, with D. Kuhn, S. W. Wallace and S. Zymler. Mathematical Programming A, 2014.

Conference Papers

• DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty, with M. Cui, D. Zhou, Y. Han, Q. Wang, H. Zhang, Z. Zhou. ICLR, 2026.

• Distributionally Robust Performative Optimization, with Z. Jia, Y. Wang and R. Dong. NeurIPS, 2025.

• Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification, with D. Zhou, J. Chavez, H. Chen, and H. Zhang. NeurIPS, 2025.

• Robust System Identification: Finite-Sample Guarantees and Connection to Regularization, with H. Park and Y. Li. ICLR, 2025.

• Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes, with D. Zhou, C. Brix, and H. Zhang. NeurIPS, 2024.

• Distributionally Robust Path Integral Control, with H. Park, D. Zhou, and T. Tanaka. ACC, 2024.

• Learning Fair Policies for Multi-stage Selection Problems from Observational Data, with Z. Jia, P. Vayanos and W. Xie. AAAI, 2024. Oral Presentation

• Two-stage Optimization for Aerocapture Guidance, with E. M. Zucchelli, B. A. Jones and E. Mooij. AIAA Scitech Forum, 2021.

• Transmission Switching under Uncertain Wind using Linear Decision Rules, with Y. Zhou and H. Zhu. IEEE Power & Energy Society General Meeting (PESGM), 2020.

• Robust Data-Driven Dynamic Programming, with D. Kuhn. Neural Information Processing Systems (NIPS), 2013. [poster][code]

• Risk-averse Shortest Path Problems, with C. Gavriel and D. Kuhn. IEEE Conference on Decision and Control (CDC), 2012.

• Ink Bleed Reduction using Functional Minimization, with Z. Wu and M. S. Brown. IEEE Computer Vision and Pattern Recognition (CVPR), 2010. [poster]

• A Chopper Stabilized Pre-amplifier for Biomedical Signal Acquisition, with Y. Zheng. IEEE International Symposium on Integrated Circuits, 2008.

• A Micropower CMOS Amplifier for Portable Surface EMG Recording, with P. K. Chan, H. B. Tan and V. K. S. Ong. IEEE Asia Pacific Conference on Circuits and Systems, 2006.