Track Description

The explosion in the amount of digitized data in recent years has created a huge opportunity to obtain insights from data and to make predictions. However, it has brought forth several challenges related to data processing and knowledge creation given the volume, velocity and variety of various new types of data. These challenges include our ability to analyze this data and extract useful information comprehensively and speedily, create high performance models for reliable predictive and prescriptive applications as well as draw strong causal inferences. Further complicating the situation is that these new datasets and their applications often defy conventional statistical assumptions underpinning many existing data analysis methods.

Analytics has emerged as a major area of Data Science which uses scientific methods for extracting knowledge and insights from various forms of structured and unstructured data. Analytics involves the discovery, interpretation, communication, and leveraging of meaningful patterns in the data. This track is dedicated to research developing novel data science and analytics theories, algorithms, and methods to solve challenging and practical problems that benefit business and society at large. We invite innovative data science and analytics research work that address the related data challenges from the lens of statistics, data mining, machine learning, artificial intelligence, econometrics, and psychometrics, among others. We are looking for original research addressing these challenges in domains such as marketing, operations, finance, health care, and energy, and applications such as fraud detection, social network services, talent analytics, privacy, recommendation systems, etc. Contributions on novel methods may be motivated by insightful observations on the shortcomings of state-of-the art approaches in addressing practical challenges, or may identify entirely novel data science problems. Research contributions on theoretical and methodological foundations of data science, such as optimization for machine learning or new algorithms for data mining or new approaches for causal inference are also welcome.

Track Chairs
Sumit Sarkar
J. Leon Zhao
GOH Khim Yong
Ashish Agarwal
Schedule

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A Decision Tree Approach for Assessing and Mitigating Background and Identity Disclosure Risks

Haifang Yang, Dalian University of Technology
Mingzheng Wang, Zhejiang University
Xiangpei Hu, Dalian University of Technology
Xiaobai Li, University of Massachusetts Lowell

Acquiring Heterogeneous Customer Data for Business Analytics

Xiaoping Liu, Northeastern University
Xiaobai Li, University of Massachusetts Lowell

Advancing Recommendations on Two-Sided Platforms: A Machine Learning Approach to Context-Aware Profiling

Schahin Tofangchi, Digital Transformation Research Center
André Hanelt, University of Kassel
Siyuan Li, William & Mary

Affordable Uplift: Supervised Randomization in Controlled Experiments

Johannes Haupt, Humboldt-Universität zu Berlin
Daniel Jacob, Humboldt-Universität zu Berlin
Robin Gubela, Humboldt-Universität zu Berlin
Stefan Lessmann, Humboldt-Universität zu Berlin

Application of Deep User Activity Transfer Models for Cross Domain User Matching

Sapumal Ahangama, National University of Singapore
Danny Chiang Choon Poo, National University of Singapore

Assigning Course Schedules: About Preference Elicitation, Fairness, and Truthfulness

Sören Merting, Technical University of Munich
Martin Bichler, Technical University of Munich
Aykut Uzunoglu, University of Augsburg

Battling Alzheimer’s Disease through Early Detection: A Deep Multimodal Learning Approach

Lin Qiu, National University of Singapore
Vaibhav Rajan, National University of Singapore
Bernard Tan, National University of Singapore

Can Mobile App Usage Help Predict Firm-Level Stock Returns?

Ziqing Yuan, City University of Hong Kong
Hailiang Chen, The University of Hong Kong

CHAMALEON: Framework to improve Data Wrangling with Complex Data

Álvaro Valencia-Parra, University of Seville
Ángel Jesús Varela-Vaca, University of Seville
María Teresa Gómez-López, University of Seville
Paolo Ceravolo, The University of Milan

Counterfactual Explanations for Data-Driven Decisions

Carlos Fernandez, New York University
Foster Provost, New York University
Xintian Han, New York University

Deep Recurrent Neural Networks for Mortality Prediction in Intensive Care using Clinical Time Series at Multiple Resolutions

Suparna Ghanvatkar, National University of Singapore
Vaibhav Rajan, National University of Singapore

Detecting Senior Executives’ Personalities for Predicting Corporate Behaviors: An Attention-based Deep Learning Approach

Kai Yang, City University of Hong Kong
Raymond Lau, City University of Hong Kong

Different Prices for Different Customers – Optimising Individualised Prices in Online Stores by Artificial Intelligence

Alper Be?er, Technische Universität Dortmund
Richard Lackes, Technische Universität Dortmund
Markus Siepermann, Technische Universität Dortmund

Discovering Barriers to Opioid Addiction Treatment from Social Media: A Similarity Network-Based Deep Learning Approach

Jiaheng Xie, University of Arizona
Zhu Zhang, Chinese Academy of Sciences
Xiao Liu, University of Utah
Daniel Zeng, University of Arizona

Hospital Reliability Evaluation in Chinese Context: A Study from the Decision Theory Perspective

Wenping Zhang, Renmin University of China
Hui YUAN, City University of Hong Kong
Wei Xu, School of Information
Raymond Lau, City University of Hong Kong

Leveraging Deep-learning and Field Experiment Response Heterogeneity to Enhance Customer Targeting Effectiveness

Kunpeng Zhang, University of Maryland
Xueming Luo, Temple University

Mechanisms for Automatic Training Data Labeling for Machine Learning

Yang Gu, University of Arizona
Gondy Leroy, University of Arizona

Mining Online Reviews to Uncover Consumer Brand Engagement

Uday Kulkarni, Arizona State University
Amit Deokar, University of Massachusetts Lowell
Haya Ajjan, Elon University

Predicting Violent Crime with Gang Social Media Postings

Sherry Fowler, UNC Charlotte
Antonis Stylianou, UNC Charlotte
Dongsong Zhang, UNC Charlotte
Shannon Reid, UNC Charlotte
Reza Mousavi, UNC Charlotte

Robust Local Explanations for Healthcare Predictive Analytics: An Application to Fragility Fracture Risk Modeling

Buomsoo (Raymond) Kim, University of Arizona
Karthik Srinivasan, University of Arizona
Sudha Ram, University of Arizona

Same Same but Different? The Predictive Power of Association Types in Brand Buzz for Investor Returns

Stefan Fischer, University of Goettingen
Welf Weiger, University of Goettingen
Maik Hammerschmidt, University of Goettingen

The Impact of Investors’ Surprise Emotion on Post-M&A Performance: A Social Media Analytics Approach

Qiping WANG, City University of Hong Kong
Raymond Lau, City University of Hong Kong

The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews

Bernhard Lutz, University of Freiburg
Nicolas Pröllochs, University of Giessen
Dirk Neumann, University of Freiburg

The Price of Fairness - A Framework to Explore Trade-Offs in Algorithmic Fairness

Christian Haas, University of Nebraska at Omaha

The Value of Alternative Data in Credit Risk Prediction: Evidence from a Large Field Experiment

Tian Lu, Carnegie Mellon University
Yingjie Zhang, University of Texas at Dallas
Beibei Li, Carnegie Mellon University

Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation

Patrick Zschech, Technische Universität Dresden
Jonas Bernien, Technische Universität Dresden
Kai Heinrich, Technische Universität Dresden

Towards Deep Learning Interpretability: A Topic Modeling Approach

Yidong Chai, Tsinghua University
Weifeng Li, University of Georgia

Transfer Learning in Dynamic Business Environments: An Application in Earnings Forecast for Public Firms

Jiaxu Peng, School of Computing, National University of Singapore

Trust and Distrust in Big Data Recommendation Agents

Heverton Roberto de Oliveira Cesar de Moraes, Fundacao Getulio Vargas
Otavio Sanchez, Fundacao Getulio Vargas - FGV
Susan Brown, University of Arizona
Bin Zhang, University of Arizona

Uncovering Latent Archetypes from Digital Trace Sequences: An Analytical Method and Empirical Example

Aaron Schecter, University of Georgia
Noshir Contractor, Northwestern University