Harvey J Greenberg Research Award Announcement: Nominations due June 30, 2023
The Harvey J Greenberg Research Award is a recently established annual award that honors research excellence in the field of computation and operations research applications, especially those in emerging application fields. Honored research would focus on contributions that exhibit the promise of making a significant impact in the scope of OR/MS/Analytics practice.
The award is accompanied by a certificate and a $1,000 honorarium.
The award will be presented at the ICS Business Meeting that will take place during the 2023 INFORMS Annual Meeting in Phoenix, October 15-18, 2023. For more details on this award and its generous sponsors, check out its website https://connect.informs.org/computing/awards/greenberg-research-award.
Conditions for eligibility:
At least one author on a submitted paper must be an INFORMS member. Individuals who won the ICS Prize within two years of the submission date, or who are chosen to win the ICS Prize in the current award year, are ineligible for the award.
A submission for this award consists of (1) a single research paper and (2) a summary statement prepared for the award committee. The paper must have been accepted for publication at the time of submission in a refereed journal, book, or conference proceeding. If published, the paper must have appeared in the literature in the year of the award or in the two calendar years preceding the award year. The summary statement must be a brief (no more than 200 words) statement summarizing the innovation and impact of the submitted paper.
All nominations should be submitted electronically to the committee chair by 11:59 pm EDT, June 30, 2023. All submissions will be acknowledged by the committee chair.
The 2023 Harvey J Greenberg Research Award Committee:
2022 Harvey J Greenberg Research Award
Winners: Zeyu Liu and Anahita Khojandi and Xueping Li and Akram Mohammed and Robert L Davis (Tennessee), and Rishikesan Kamaleswaran (Emory) for their paper "A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction," INFORMS Journal on Computing, published online March 22,2022.
Sepsis can be triggered by the body's extreme response to an infection and can be life-threatening. Existing sepsis prediction algorithms suffer from high false-alarm rates. The authors present an integrated machine learning (ML) and partially observable Markov decision process framework to address this issue. This approach is calibrated and tested using physiological data collected from bedside monitors. The framework reduces false-alarm rates and improves sepsis prediction accuracy compared to existing ML benchmarks. This is a comprehensive paper with novel contributions to computing and important practical implications. The committee members commend the authors for this excellent work.
Honourable Mention: Eyyüb Y. Kıbıs ̧ ̇I. Esra Büyüktahtakın, Robert G. Haight, Najmaddin Akhundov, Kathleen Knight, and Charles E. Flower, for their paper "A Multistage Stochastic Programming Approach to the Optimal Surveillance and Control of the Emerald Ash Borer in Cities,", INFORMS Journal on Computing, 33(2), 2021.
Committee: Archis Ghate, Chair (Washington), Ariela Sofer (George Mason), David Woodruff (UC Davis)
2021 Harvey J Greenberg Research Award
Winners: Hamidreza Validi, Austin Buchanan, and Eugene Lykhovyd for their paper "Imposing Contiguity Constraints in Political Districting Models," to appear, Operations Research, 2021.
A classical problem in operations research that concerns the generation of political districting maps; early approaches using integer programming date back to the 1960s. In this well-rounded and timely paper, the authors survey modern attempts to add contiguity constraints to these classical models and propose two new formulations that are shown to impose contiguity in an easier way. The authors investigate the theoretical relationships between these models, discover new sets of cutting planes, and develop Lagrangian techniques to fix variables. With these innovations, they solve, for the first time, optimally compact districting maps for 21 US states at the census tract level. Their source code, models, and data are publicly available. The selection committee believes that this paper represents the spirit of Harvey Greenberg's work: tackling problems of societal impact with real-life data using state-of-the-art operations research techniques, providing an intriguing example of OR in practice.
Committee: Pascal van Hentenryck (Chair), Sven Leyffer, Alice Smith
2020 Harvey J Greenberg Research Award
Winners: Danial Davarnia and Willem-Jan van Hoeve for their paper "Outer Approximation for Integer Nonlinear Programs via Decision Diagrams," forthcoming in Mathematical Programming Series A.
Committee: Dorit Hochbaum (chair), Pascal van Hentenryck, Karla Hoffman