WITS2021 Awards


WITS Facebook Page

Best Reviewers

Kai-Lung Hui, Hong Kong University of Science and Technology

Debra Vander Meer, Florida International University

Keran Zhao, University of Illinois at Chicago

Best dissertation

Pan Li, New York University

For thesis entitled Multi-Faceted Consumer Preferences: Incorporating Unexpectedness and Cross-Domain Information into Design of Recommender System

Best dissertation runner up

Xiang (Shawn) Wan, University of Florida

For thesis entitled The Economic Value of Algorithmic Product Recommendation Systems

Best student paper

Jujun Huang, Stevens Institute of Technology

Rong Liu, Stevens Institute of Technology

For paper entitled Leverage Disclosure Change Trajectories to Detect Financial Frauds

Best student paper runner up

Pan Li, New York University

Alexander Tuzhilin, New York University

For paper entitled Exploring Consumer Trajectories in Recommender System: A Deep Reinforcement Learning Approach

Best paper

Jiaheng Xie, University of Delaware

Yidong Chai, Hefei University of Technology

Xiao Liu, Arizona State University

For paper entitled An Interpretable Deep Learning Approach to Understand Health Misinformation Transmission on YouTube

Best paper runner up

Maria De-Arteaga, University of Texas at Austin

Artur Dubrawski, Carnegie Mellon University

Alexandra Chouldechova, Carnegie Mellon University

For paper entitled Leveraging Expert Consistency to Improve Algorithmic Decision Support

Design science award winners

Team 1

Alok Gupta, University of Minnesota

Wolfgang Ketter, University of Cologne and Erasmus University Rotterdam

Yixin Lu, George Washington University

Eric van Heck, Erasmus University Rotterdam

For work entitled Designing Next Generation High-Speed Auction Markets (Next-ACT)

Team 2

Soumya Sen, University of Minnesota

Pinar Karaca-Mandic, University of Minnesota

Archelle Georgiou, Starkey Technologies

Yi Zhu, University of Minnesota

For work entitled An Information System for COVID-19 Hospitalization Tracking and Analysis