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OIS Faculty Present Papers at INFORMS Annual Meeting

Manning School Sponsors Data Mining and Decision Analytics Workshop

OIS faculty hold a meeting Photo by Ed Brennen
Operations and Information Systems faculty members meet at the Pulichino Tong Business Center.

06/12/2019
By Ed Brennen

The Manning School of Business was well represented at the 2018 annual meeting of the Institute for Operations Research and Management Sciences (INFORMS), the world’s largest professional association of operations research and analytics professionals and students.

Assoc. Prof. Asil Oztekin and Asst. Prof. Nichalin Summerfield, both from the Operations and Information Systems department, presented their paper “A Holistic Data Analytic Approach to Determine Impacts of the Caregiver Advise, Record, Enable (CARE) Act on Reducing Readmission and Mortality Rates among Older Adults.”

OIS Prof. Bob Li, meanwhile, served as program co-chair for the Data Science workshop. The meeting was held last November in Phoenix.

The Manning School was also a sponsor of the 13th Workshop on Data Mining and Decision Analytics, which was held on the eve of the INFORMS annual meeting on Nov. 3.

Oztekin, Summerfield, Neha Ajgaonkar ’18 and Ali Dag presented their paper “Predicting Patient No-Shows via a Hybrid Business Analytics Methodology” at the workshop, which was attended by more than 100 participants.

“I’d like to thank all the OIS faculty for representing us so well with their INFORMS engagement,” Manning School Dean Sandra Richtermeyer.

Oztekin recently completed a one-year term as chair of the Data Mining section of INFORMS, the largest data mining community in the world for business schools and industry experts.  

The Data Mining cluster included a record 74 technical sessions, many of which included collaborations with other INFORMS subdivisions such as Artificial Intelligence; Health Applications; Quality, Statistics and Reliability; and Energy, Natural Resources and the Environment. It also included 36 sessions in the newly introduced “practice curated track.” The sessions covered topics including machine learning, Big Data, probability and statistics, and interplay between data mining and optimization.