Track Description
This track is dedicated to research that applies and/or develops novel data science and analytics theories, algorithms, and methods to identify and solve challenging and practical problems that benefit business and society at large. Domains may include small businesses, healthcare, judicial systems, social media and energy, and applications such as fraud detection, social network services, human resource analytics, privacy, recommendation systems, etc. Contributions may be motivated by shortcomings of state-of-the art approaches in addressing practical challenges, or may apply novel data science tools to existing problems. This track is open to all types of research, including conceptual, theoretical, analytical, and/or empirical.

Track Chairs:
Pei-yu (Sharon) Chen, Arizona State University
InduShobha Chengalur-Smith, University at Albany – SUNY
T. Ravichandran, Rensselaer Polytechnic Institute
Bo Sophia Xiao, University of Hawaii at Manoa

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Sunday, December 12th

A Framework for Optimal Crowdsourcing Contest Design

Wangsheng Zhu, University of Texas at Dallas
Jiahui Mo, Clemson University
Syam Menon, The University of Texas at Dallas
Sumit Sarkar, University of Texas at Dallas

COVID-19, Urban Transportation, and Air Pollution

Juan Wang, University of Science and Technology of China
Yifan Yu, University of Washington
Wendao Xue, University of Washington
Yong Tan, University of Washington

Dependency Modeling with Copulas in Multi-Armed Bandits

Siva Rajesh Kasa, National University of Singapore
Vaibhav Rajan, National University of Singapore

Event-Driven Assessment of Currency of Wiki Articles: A Novel Probability-Based Metric

Mathias Klier, University of Ulm
Lars Moestue, University of Ulm
Andreas Alexander Obermeier, University of Ulm
Torben Widmann, University of Ulm

Gendered Language in Resumes -- An Empirical Analysis of Gender Norm Violation and Hiring Outcomes

Prasanna Parasurama, New York University
João Sedoc, New York University

Generalist Leaders, Specialized Subordinates - An Ensemble Learning Approach for Bankruptcy Prediction

Joel Quek, National University of Singapore
Ke-Wei Huang, National University of Singapore

GroupFM: Enabling Context-Aware Group Recommendations with Factorization Machines

Michael Szubartowicz, University of Regensburg

Improving Explainability and Accuracy through Feature Engineering: A Taxonomy of Features in NLP-based Machine Learning

Thiemo Wambsganss, University of St. Gallen
Christian Engel, University of St. Gallen
Hansjörg Fromm, Karlsruhe Institite of Technology

Insurance Fraud and Isolation Forests

Jörn Debener, University of Muenster
Volker Heinke, University of Muenster
Johannes Kriebel, University of Muenster

Intention-based Deep Learning Approach for Detecting Online Fake News

Kyuhan Lee, University of Arizona
Sudha Ram, University of Arizona

Predicting Store Closures Using Urban Mobility Data and Network Analysis

Tal Shoshani, Tel Aviv University
Peter Pal Zubcsek, Tel Aviv University
Shachar Reichman, Tel Aviv University

Relational Time Series Forecasting for Retail Drugstores: A Graph Neural Network Approach

Jing Liu, Fudan Unviersity
Gang Wang, Hefei University of Technology
Lihua Huang, Fudan University

Strategic Decision Support System for Fleet Investments in the Vaccine Supply Chain

Felix Oberdorf, Julius-Maximilians-University
Peter Wolf, Julius-Maximilians-University
Myriam Schaschek, Julius-Maximilians-University
Nikolai Stein, Julius-Maximilians-University

SumExp: A Summarization-Based Approach for Explaining NLP Models

Diana Hristova, HWR Berlin
Mohd Saif Khan, Bauhaus University

The Sales Data Sells: Effects of Real-Time Sales Analytics on Live Streaming Selling

Yumei He, University of Houston
Lingli Wang, Tsinghua University
Nina Huang, University of Houston
Yili Hong, University of Houston
Jiandong Ding, Alibaba Group
Yan Sun, Alibaba Group
Yingyao Liu, Alibaba Group

Understanding the Role of Video Quality and Emotion in Live Streaming Viewership

Keran Zhao, University of Illinois at Chicago
Yuheng Hu, University of Illinois at Chicago
Yingda Lu, University of Illinois at Chicago

What Types of Crowd Generate More Valuable Content? Evidence from Cross-Platform Posting

Xiaohui Zhang, Arizona Sate University
Qianzhou Du, Nanjing University
Zhongju Zhang, Arizona State University