Grani A. Hanasusanto

 

Assistant Professor
Operations Research and Industrial Engineering
The University of Texas at Austin
ETC 5.120
204 E. Dean Keeton St. Stop C2200
Austin, TX 78712-1591
E-mail: grani.hanasusanto@utexas.edu
Phone: +1 (512) 471-3078

Bio

Grani A. Hanasusanto is an Assistant Professor of Operations Research and Industrial Engineering at The University of Texas at Austin (UT). Before joining UT, he was a postdoctoral researcher 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 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

Conference Papers

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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