Faculty Members Earn Distinction at 2022 INFORMS Annual Meeting

At the University of Maryland’s Robert H. Smith School of Business, cutting-edge research is paving the way for real-world impact. And three Maryland Smith faculty members were recognized for their research efforts at the 2022 INFORMS Annual Meeting in Indianapolis on Oct. 16-19, 2022.

Smith Chair of Management Science Michael Fu, was presented with the George E. Kimball Medal for recognition of distinguished service to the Institute for Operations Research and the Management Sciences (INFORMS), the operations research profession and that of management sciences. The recognition comes just a year after Fu received the Institute’s Saul Gass Expository Writing Award, in which he became just the second Maryland Smith professor to earn the distinction.

The Institute cited Fu as “an exemplary academic when it comes to professional service – a model for young academics to emulate.” This international group also said the “altruistic character traits that permeate his personal life are the very same ones that have made him a valuable and dedicated contributor to INFORMS.”

Also receiving recognition during the event were professors Xiaojia Guo and Raghu Raghavan. Guo won the 2022 INFORMS Aviation Applications Section Best Paper Award for her work,

"Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning”, published in the journal, Manufacturing and Service Operations Management (MSOM) last year.

Raghavan received runner-up honors for the INFORMS Computing Society Prize, which is awarded to the best paper(s) in the field pertaining to the Operations Research/Computer Science interface. The recognition came as a result of his contributions to a series of papers titled “Influence Diffusion on Social Networks”.

He was also a finalist for the INFORMS Telecommunications Section Best Paper Award for the most outstanding published paper applying operations research techniques in the context of telecommunications – Selected paper: “Influence Maximization with Latency Requirements on Social Networks”.