2021 INFORMS Poster Competition WinnersWe are delighted to announce the 2021 INFORMS Poster Competition Winners:
First Place:"Holistic AI for Wildlife Analytics"
by Leonard Boussioux form MIT, USA and Charles A. Kantor from Stanford University, USA.
While worldwide ecosystems face a mass extinction of species, demographic data related to shifts in species diversity and abundance has substantial taxonomic, spatial, and temporal biases and gaps. Available methods to study and monitor species and their population trends are often antiquated and inefficient. There is a need for efficient, rigorous, and reliable methods to study and monitor wildlife. We introduce a systematic and holistic framework to build efficient AI tools adapted to wildlife data, challenges, and needs. We illustrate our methodologies with real-world datasets provided by several museums and crowdsourcing platforms and show the impact of our state-of-the-art tools.
Second Place:"Spatial Pricing of Ride-sourcing Services in A Congested Transportation Network"
by Fatima Afifah and Zhaomiao Guo from University of Central Florida, USA.
We investigate the impacts of spatial pricing on ride-sourcing services in a Stackelberg framework considering traffic congestion. In the lower level, we use combined distribution and assignment approaches to explicitly capture the interactions between drivers' relocation, riders' mode choice, and all travelers' routing decisions. In the upper level, a transportation network company (TNC) determines spatial prices to minimize imbalance in a two-sided market. We show the existence of the optimal pricing strategies for locational imbalance minimization, and propose effective algorithms with reliable convergence properties.
Third Place:"Multi-Branching Temporal Convolutional Network for Sepsis Prediction"
by Zekai Wang and Bing Yao from Oklahoma State University, USA.
Sepsis is among the leading causes of morbidity and mortality in modern intensive care units. Accurate sepsis prediction is of critical importance to save lives and reduce medical costs. However, real-world medical data are often complexly structured with a high level of uncertainty (e.g., missing values, imbalanced data). In this paper, we propose a novel predictive framework with Multi-Branching Temporal Convolutional Network (MB-TCN) for robust prediction of sepsis. The MB-TCN framework efficiently handles the missing value and imbalanced data issues. Experimental results show that MB-TCN outperforms existing deep learning methods.
Our heartfelt congratulations!
We sincerely thank all the participants for their effort in presenting their research as a poster and go through all the logistics of handling their part online flawlessly. Likewise, we greatly appreciate the time and effort of the judges who took the time from their busy schedules to review the posters and accompanying videos as well as to meet with the presenters to evaluate the posters on multiple criteria in an online fashion.
Halit Uster (SMU) and Renata Konrad (WPI)
INFORMS 2021 Poster Competition Co-Chairs