This prize is awarded annually to the company that effectively integrates analytics into organizational decision-making, and has repeatedly applied ORMS principles in pioneering, novel and lasting ways. The 2021 prize winners, Amazon will describe their innovative O.R. work in the INFORMS Prizes and Special Sessions Track. The 2022 winner will be recognized at the Edelman Gala on Monday evening.
Previous winners include BNSF Railway, Disney, U.S. Air Force, GM, Chevron, Memorial Sloan-Kettering Cancer Center, Sasol, Jeppesen, Intel, General Electric Global Research Center, Schneider National, Air Products and Chemicals, Procter & Gamble, UPS and other leading companies.
UPS George D. Smith Prize
The George D. Smith Prize is aimed at strengthening ties between academia and industry by rewarding institutions of higher education for effective and innovative preparation of students to be good practitioners of operations research. The Prize is generously underwritten by UPS. Awarded for the first time in 2012, past winners are Carnegie Mellon University, H. John Heinz III College, Sauder School of Business, University of British Columbia – Center for Operations Excellence, MIT Leaders for Global Operations, Naval Postgraduate School, and Tauber Institute for Global Operations at University of Michigan.
The teams will present their work to the judges on Sunday, April 3.
The Smith Prize winner will be announced at the Edelman Gala on Monday, April 4. The 2022 winner will give their presentation on Tuesday, April 5 in the INFORMS Prizes & Special Sessions Track.
Daniel H. Wagner Prize for Excellence in Operations Research
The Daniel H. Wagner competition is held each fall at the INFORMS Annual Meeting. The 2021 Wagner Prize Reprise will take place on Monday, April 4 in the INFORMS Prizes & Special Sessions Track.
The 2021 Wagner Winner is a team from the the Wharton School, University of Pennsylvania and USC Marshall School of Business for their work, “Interpretable OR for High-Stakes Decisions: Designing the Greek COVID-19 Testing System.” It was presented by Hamsa Bastani from the Wharton School, University of Pennsylvania and Kimon Drakopoulos and Vishal Gupta from USC Marshall School of Business.
This prize emphasizes the quality and coherence of the analysis used in practice. Dr. Wagner strove for strong mathematics applied to practical problems, supported by clear and intelligible writing. The Wagner Prize recognizes those principles by emphasizing good writing, strong analytical content and verifiable practice successes. The competition is held and the winner is announced at the INFORMS Annual Meeting in the fall.
Past awardees include practitioners and researchers from Lehigh University and the Pennsylvania Department of Corrections, The Forestry Research Institute of Sweden, CDC, Ford, U.S. Coast Guard, Intel, IBM T. J. Watson Research, Schneider National, Boston University, University of Florida, and others.
Innovative Applications in Analytics Award (IAAA)
Brought to you by the Analytics Society of INFORMS, Kinaxis and Adelphi University
The IAAA Finalists will present on Tuesday, April 5 in the INFORMS Prizes & Special Sessions Track. The winner will be announced at the conference.
The purpose this award is to recognize the creative and unique application of a combination of analytical techniques in a new area. The prize promotes the awareness and value of the creative combination of analytics techniques in unusual applications to provide insights and business value. The Analytics Section leadership would like to cordially thank all the members of the judging committee for their hard work in selecting these finalists.
Syngenta Crop Challenge in Analytics
Today, the agriculture industry works to optimize the amount of food we gain from plants by breeding plants with the strongest, highest-yielding genetics. Scientists at R&D organizations like Syngenta create stronger plants by breeding and then selecting the best offspring over time to provide to farmers. Data-driven strategies can help our industry breed better seeds, faster. Developing models that identify robust patterns in our experimental data may help scientists more accurately choose seeds that increase the productivity of the crops we plant – and help address the growing global food demand.
How can we use data to address the growing global food demand?