DMDAWorkshop2023

The Data Mining Society of INFORMS is organizing the 18th INFORMS Workshop on Data Mining and Decision Analytics in conjunction with the 2023 INFORMS Annual Meeting. You are cordially invited to join us and share your recent research work with peers from data mining, decision analytics, and artificial intelligence.

To participate, a full paper must be submitted before the deadline for consideration. The workshop committee also announces the best paper competition in both theoretical and applied research tracks. All accepted papers are automatically considered for the best paper competition in the chosen track. Suitable finalists will be recommended for fast-track submission to INFORMS Journal on Data Science (IJDS) or invited to submit the extended abstract to the special issue of Responsible AI and Data Science for Social Good at INFORMS Journal on Computing.

Topics of interest include, but are not limited to:

● Large-Scale Data Analytics and Big Data

● Interpretable Data Mining

● Simulation/Optimization in Data Analytics

● Network Analysis and Graph Mining

● Privacy & Fairness in Data Science

● Bayesian Data Analytics

● Healthcare Analytics

● Longitudinal Data Analysis

● Causal Mining (Inference)

● Anomaly Detection

● Deep Learning

● Emerging Data Analytics in Industrial Applications

● Analytics in Social Media & Finance

● Reliability & Maintenance

● Visual Analytics

● Web Analytics/Web Mining

● Text Mining & Natural Language Processing

● Ethics and Security in Data Mining

● Fairness in Machine Learning

● Data analytics to promote diversity, equity and inclusion (DEI)

Timeline

● June 1: Paper submission begin

● August 8: Paper submission close

● September 1: Final review decision

● September 22: Workshop on Data Mining and Decision Analytics registration deadline

We apologize for the delay, we will finalize everything asap.

Papers submission guideline

● Maximum of 10 pages (including abstract, tables, figures, and references).

● Single-spacing and 11-point font with one-inch margins on four sides.

● Papers must be submitted via this link (link expired). Late submission will not be considered for further review.

● Copyright: The DM workshop will not retain the copyrights on the papers; so, the authors are free to submit their papers to other outlets.

DM Workshop Co-Chairs

· Ying Lin, Houston University, ylin58@uh.edu

· Feng Liu, Stevens Institute of Technology, fliu22@stevens.edu

· Xiaochen Xian, University of Florida, xxian@ufl.edu

· Michael Lash, University of Kansas, michael.lash@ku.edu

Keynote Speakers

Dr. Maytal Saar-Tsechansky

University of Texas at Austin

Mr. Rishi Bhatia

Walmart Global Tech

Talk 1: Why we Need Personalized & Organizationally-Aware AI Partners & How to Produce Them?

Speaker: Maytal Saar-Tsechansky, Ph.D., Professor of School of Business, University of Texas at Austin

Abstract: Recent studies highlight the potential of AI to improve high-stakes human decisions in critical domains like healthcare. Despite these promising prospects, AI systems to advise experts in such contexts often fail to deliver tangible value to organizations. In this talk, I will first argue how key properties of AI-assisted high-stakes decision-making contexts are crucial to inform the development of AI advisors that meaningfully benefit decision-makers and organizations. State-of-the-art AI for advising experts is produced independently of the experts and of the organization they intend to benefit. However, I will demonstrate why idiosyncratic properties of these environments, such as an expert’s decision-making behaviors, the patterns shaping experts’ discretion of AI counsel, and the organization's tolerance of the inherent costs of engagement with AI to improve high-stakes decisions are crucial to inform the development of effective human-AI teams that benefit organizations. I will then present a framework that builds on these understandings to generate personalized and organizationally-aware AI advisors and will share results on its performance. Our results demonstrate not only the opportunity to amplify high-stakes decision-making in high-stakes settings, but also underscore our framework’s effectiveness at producing efficient advisors with the necessary properties to catalyze the widespread adoption of AI-assisted advising in organizations. I will conclude with a proposed AI research agenda in business for advancing practically impactful human-AI collaboration.

About Pof. Saar-Tsechansky: Maytal Saar-Tsechansky is the Mary John and Ralph Spence Centennial Professor at the McCombs School of Business at the University of Texas at Austin, and a co-founder of Sweetch, an AI-based health company. Her research focuses on advancing AI to improve decision-making and to benefit people, organizations, and society. Her recent work focuses on human-AI collaboration and trustworthy AI with the overarching goal of bringing to bear human, organizational, and societal goals and constraints to catalyze AI systems’ positive impact in the world. This agenda includes AI systems that cost-effectively learn from imperfect and biased humans, and advancing AI towards human-AI teams tasked with low- and high-stakes decision-making, involving both predictions and course of action choices. Her research has been informed and inspired by active collaborations with organizations, businesses, and domain experts. Over the years, her research addressed challenges in a variety of domains, including health care, the future of work, renewable energy, audit, and finance. Her work has been supported by government and industry, including the National Science Foundation and the Israeli Science Ministry. Maytal leads the University of Texas at Austin’s Translational AI initiative and is an academic board member of the university’s Machine Learning Lab.

Talk 2: AI Driven Decision Making

Speaker: Rishi Bhatia, M.S., Director of Data Science, Walmart Global Tech

Abstract: Humans make decisions based on experience and historical observations to optimize their objectives. AI is swiftly becoming integral to modern decision-making across various fields. This presentation will highlight insights gained from employing AI-driven strategies for large-scale decisions in traditional industries, ensuring human involvement at every stage. We'll examine case studies from key areas of merchandising, including assortment, pricing, and replenishment, and observe the effects of data-driven decisions on efficiency and operational costs. Our discussion will not only spotlight success stories but also underscore the significance of human expertise, illustrating its role as feedback for AI in the digital transformation journey. In addition, we'll delve into the technical facets of AI, elucidating machine learning algorithms and optimization models. We'll further discuss challenges with traditional methods, the integration of emerging technologies, and strategies to navigate these complexitie

About Mr. Bhatia: With over 15+ years of industry experience in data science, Rishi currently serves as the Director of Data Science at Walmart Global Tech in Bentonville. Under his leadership, an adept team of Data Scientists and Machine Learning Engineers innovate with advanced machine learning models and optimization solutions. These innovations primarily focus on assortment planning, space optimization, and pricing, translating to notable increments in sales and significant cost reductions. Before his role at Walmart Global Tech, Rishi led as the Director of Data Science at Sam's Club, a Walmart division. In this capacity, he led teams that devised machine learning models and strategies for Membership, Marketing, and Merchandising. Rishi's pre-Walmart ventures saw him in the consulting domain, providing data science solutions to diverse industries, including retail, technology, and CPG. He also taught as a professor in mathematics and computer science. Academically, Rishi possesses dual master’s degrees in computer applications and mathematics from Guru Nanak Dev University, India.

Panel Discussion: Generative AI: Opportunities and Effects on DMDA Research

Dr. Tinglong Dai

Johns Hopkins University

Dr. Anjana Susarla

Michigan State University

Dr. Maytal Saar-Tsechansky

University of Texas at Austin

Panelists:

Tinglong Dai, Ph.D., Professor of Operations Management & Business Analytics, Johns Hopkins University
Anjana Susarla, Ph.D.,Professor of Responsible AI at the Eli Broad College of Business, Michigan State University
Maytal Saar-Tsechansky, Ph.D., Professor of School of Business, University of Texas at Austin

About Dr. Dai: Tinglong Dai is a Professor of Operations Management & Business Analytics at the Johns Hopkins Carey Business School. He co-chairs the Johns Hopkins Workgroup on AI and Healthcare and serves on the leadership team of the Hopkins Business of Health Initiative (HBHI). Dr. Dai's research interests span across human-AI interaction, global supply chains, and healthcare analytics. Dr. Dai’s current work focuses on incorporate AI into clinical workflows and advance healthcare productivity, access, and equity. He has published extensively in leading journals such as Management Science, M&SOM, Marketing Science, and Operations Research. He has received numerous awards for his research, including the Johns Hopkins Discovery Award, INFORMS Public Sector Operations Research Best Paper Award, POMS Best Healthcare Paper Award, and Wickham Skinner Early Career Award (runner-up). In 2021, he was named one of top 40 business school professors under 40 in the world by Poets & Quants. Dr. Dai is an Associate Editor of Management Science, M&SOM, Health Care Management Science, and Naval Research Logistics, and a Senior Editor of Production and Operations Management. Dr. Dai is a sought-after expert and has been quoted thousands of times in the media, including the Associated Press, Bloomberg, CNN, Fortune, New York Times, NPR, USA Today, Wall Street Journal, and Washington Post. He has also appeared on national and international television, including CNBC, PBS NewsHour, and Sky News. Dr. Dai joined Johns Hopkins in 2013 after receiving his Ph.D. in Operations Management and Robotics from Carnegie Mellon University. He serves on the Executive Committee of the Johns Hopkins Institute for Data Intensive Engineering and Science (IDIES) and holds joint faculty appointments at the Johns Hopkins School of Nursing and the Center for Digital Health and Artificial Intelligence.

About Dr. Susarla: Anjana Susarla is the Omura-Saxena Professor of Responsible AI at the Eli Broad College of Business at Michigan State University. She earned an undergraduate degree in Mechanical Engineering from the Indian Institute of Technology, Chennai; a graduate degree in Business Administration from the Indian Institute of Management, Calcutta; and Ph.D. in Information Systems from the University of Texas at Austin. Her work has appeared in several academic journals and peer-reviewed conferences. She has been interviewed in, had her research quoted and op-eds published in several media outlets such as the Associated Press, BBC, Fast Company, Fox News, National Public Radio, NBC, Newsweek and Washington Post. Her research has been funded by prestigious organizations such as the National Institute of Health (NIH).