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News

• May 2024: I will be presenting our work on “Distributionally Robust Path Integral Control” at the Robust Optimization Webinar.

• Apr 2024: Our paper on “Distributionally Robust Observable Strategic Queues” is accepted in Stochastic Systems.

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

• Mar 2024: Our paper on “Wasserstein Robust Classification with Fairness Constraints” is accepted in Manufacturing & Service Operations Management.

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

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

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

• June 2023: Our paper on “Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming” is accepted 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” is published in Operations Research.

• April 2022: Thank you NSF for supporting our I-Corps project on Data-Driven Robust Optimization Technology for Battery Storage System Management.

• February 2022: Thank you NSF for supporting our work on Interpretable Fair Machine Learning: Frameworks, Robustness, and Scalable Algorithms.

• January 2022: Weijun Xie and I are selected as INFORMS Diversity, Equity, and Inclusion (DEI) Ambassadors to initiate the INFORMS DEI Best Student Paper Award.

• September 2021: I have been invited as a speaker at the Centre de Recherches Mathématiques workshop on Optimization under Uncertainty. I will be presenting our work on “Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach.”

• July 2021: I am presenting our work on “Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming” at the mini-symposiums at SIAM Conference on Optimization and SIAM Conference on Control and Its Applications.

• Prateek Srivastava has successfully defended his thesis on "Robust Solution Schemes for Clustering and Decision-Making Problems under Uncertainty.” Congratulations!

• Our paper on “Finding Minimum Volume Circumscribing Ellipsoids Using Generalized Copositive Programming” is accepted in Operations Research.

• Prateek Srivastava receives an Honorable Mention at the 2020 INFORMS Computing Society Student Paper Competition for the work on “A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers.” Congratulations!

Publications

Preprints

• Distributionally Robust Optimization with Decision-Dependent Information Discovery, with Q. Jin and A. Georghiou and P. Vayanos. Available online, 2024.

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

• Second-order Bounds for the M/M/s Queue with Random Arrival Rate, with W. van Eekelen, J. J. Hasenbein, and J. van Leeuwaarden. Available online, 2023.

• Distributionally Robust Path Integral Control, with H. Park, D. Zhou, and T. Tanaka. Available online, 2023.

• 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.

• On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach, with P. Srivastava, Y. Wang, and C. P. Ho. Major revision in Operations Research, 2021.

Journal Papers

• 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.

• 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.

• 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

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

• Learning Fair Policies for Multi-stage Selection Problems from Observational Data, with Z. Jia, P. Vayanos and W. Xie. AAAI Conference on Artificial Intelligence (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.

Misc

Prospective students:

• I am always looking for outstanding and self-motivated graduate students with strong mathematical backgrounds and exceptional computer programming proficiency. Optimization under uncertainty is an exciting research area with plenty of challenges to address. If you are interested in working with me, please apply here and mention my name in the Faculty Interest field.

• Unfortunately, I am unable to respond to individual email inquiries. Once you are admitted to our graduate program, however, I would be happy to discuss the possibility of working together and the potential research projects.

• I am currently unable to accept any teaching assistants, interns, or visiting scholars.