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Bio

Grani A. Hanasusanto is an Associate Professor of Industrial & Enterprise Systems Engineering at University of Illinois Urbana-Champaign (UIUC). Before joining UIUC, he was an Assistant Professor at The University of Texas at Austin and a Postdoctoral Scholar at the College of Management of Technology at École Polytechnique Fédérale de Lausanne. He holds a PhD degree in Operations Research from Imperial College London and an MSc degree in Financial Engineering from the National University of Singapore. He is the recipient of the 2018 NSF CAREER Award. His research focuses on the design and analysis of tractable solution schemes for decision-making problems under uncertainty, with applications in operations management, energy systems, finance, machine learning and data analytics.

News

• 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”!

• November 2022: I will be participating in the Daghstuhl Seminar on “Optimization at the Second Level.”

• 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!

• UT ORIE is offering a new Concentration in Data Analytics! The concentration provides the necessary coursework and training for students who wish to develop their expertise in data science, and prepares them for a successful career in the field. Please consider applying!

• September 27, 2019: I am presenting our work on “Copositive Programming Approaches for Robust Optimization and Löwner-John Ellipsoid Problems” at the University of Texas at San Antonio.

• I am organizing the stream on “Continuous Optimization under Uncertainty” at the 22nd Conference of the International Federation of Operational Research Societies. The triennial conference will be held on June 21-26, 2020 in Seoul, South Korea.

• Areesh Mittal has successfully defended his thesis on “Copositive Programming Approaches for Robust Optimization and Löwner-John Ellipsoid Problems.” Congratulations!

• Our paper on “Robust Quadratic Programming with Mixed-Integer Uncertainty” is accepted in INFORMS Journal on Computing.

• Madhushini Prasad has successfully defended her thesis on “Approximation Schemes for Network, Clustering, and Queueing Models.” Congratulations!

• Our paper on “Improved Conic Reformulations for K-means Clustering” is accepted in SIAM Journal on Optimization.

• I am grateful and honored to be the recipient of a NSF CAREER Award.

• I have been invited as a speaker at the seminal workshop on Distributionally Robust Optimization in Banff, Canada. The workshop aims to advance distributionally robust optimization (DRO) as a dominant modeling paradigm for optimization under uncertainty and to lay the foundations for industry-size applications. The workshop will bring together the world's leading experts in DRO and closely related fields.

• Our paper on “Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls” is accepted in the premier INFORMS journal Operations Research.

Publications

Journal Papers

• 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.

• 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

• Distributionally Robust Observable Strategic Queues, with Y. Wang, M. N. Prasad, and J. J. Hasenbein. Major revision in Stochastic Systems, 2022.

• Wasserstein Robust Classification with Fairness Constraints, with Y. Wang and V. A. Nguyen. Major revision in Manufacturing & Service Operations Management, 2022.

• A Decision Rule Approach for Two-Stage Data-Driven Distributionally Robust Optimization Problems with Random Recourse, with X. Fan. Major revision in INFORMS Journal on Computing, 2022.

• 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.

• Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming, with G. Xu. Major revision in Operations Research, 2021.

• 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.

• 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.

• 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

• 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]

• 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.

Awards

• INFORMS Diversity, Equity, and Inclusion Ambassador (2022)

• ME Walker Scholar (2019)

• NSF CAREER Award (2018)

Misc

• Prospective students: I am always looking for outstanding and self-motivated graduate students with strong mathematical background and exceptional proficiency in computer programming. Optimization under uncertainty is an exciting research area with plenty of challenges remain to be addressed. If you are interested in working with me, please apply here. Unfortunately, I am currently unable to take on any teaching assistants, interns, and/or visiting scholars.

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