Analytics and Data Science

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

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Schedule
	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

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