INFORMS 10th Workshop on Data Science
Hosted by The INFORMS College on Artificial Intelligence
Conference date: Saturday, October 31, 2026
Location: San Francisco, California
Conference website: https://sites.google.com/view/data-science-2026

The 10th INFORMS Workshop on Data Science (DS 2026) is a premier research conference dedicated to developing data science theories, methods, and algorithms to solve challenging problems and benefit businesses and society at large. The workshop invites innovative data science research contributions that address business and societal challenges from the lens of statistical learning, machine learning, deep learning, reinforcement learning, large language models, generative AI, and network science. The workshop welcomes original research (complete papers or short papers) addressing non-trivial data analytical challenges and problems in marketing, finance, supply chain, healthcare, energy, cybersecurity, social networks, etc. We welcome research on novel methods that either identify and address shortcomings in current data science techniques or explore completely new problems. Additionally, we encourage submissions of research that incorporates existing methods and models (e.g., large language models) tailored to the unique needs of an application area. Research contributions on theoretical and methodological foundations of data science, such as optimization for machine learning and new algorithms or architectures for deep learning, are also welcome. Finally, we solicit submissions describing designs and implementations of data science solutions and AI systems demonstrating business or real-world impact in practical industrial applications.

Research Contributions May Include:
● Advances in deep learning techniques, e.g., transformers, graph neural networks, and embedding approaches, for targeted business applications
● Applications and adaptations of generative AI and large language models (e.g., RAG, fine-tuning, prompt engineering, tool use, agentic frameworks) for domain-specific business and societal challenges
● Statistical inference and learning methods, e.g., causal inference, Bayesian methods, and high-dimensional statistics
● Algorithmic fairness, bias mitigation, and ethical frameworks for responsible AI
● AI-powered knowledge graphs and reasoning
● Agentic AI systems and autonomous decision-making agents
● Implications of AI, including generative AI and large language models, on individuals, organizations, and societies
● AI for environmental sustainability and climate change
● AI in digital marketing and consumer behavior analysis
● Human-AI collaboration and augmented intelligence
● Explainable AI, interpretability, and computational approaches for measuring and enhancing trust in AI systems
● Computational methods for big data, text mining, natural language processing, and large language models
● Innovative methods for social network analytics on individuals and firms
● Novel data-driven approaches for cybersecurity, privacy, healthcare (e.g., chronic disease management, preventative care), and industrial applications (e.g., energy, education, finance, supply chain)
● Large-scale recommendation systems and social media systems
● Multimodal analytics for business data across text, image, video, and audio formats
● Synthetic data generation and privacy-preserving data science
● Real-world experiences with AI and ML implementations in organizations
● Applications of data science across various sectors, including healthcare, finance, marketing, energy, operations, and supply chain

Submission website: https://cmt3.research.microsoft.com/DATASCIENCE2026
Important Dates:
Paper Submission Open: May 23, 2026
Paper Submission Deadline: July 1, 2026 Extended: July 12, 2026
Notification of Paper Acceptance: August 25, 2026
Workshop Date: October 31, 2026

Information for Authors:
● Submissions in the form of complete papers or short papers are welcome.
● Complete paper submissions should be a maximum of 10 pages, including tables and figures.
● Short paper submissions (which could be extended abstracts or work-in-progress papers) should be a maximum of 5 pages, including tables and figures. Real-world applications of AI in industry can be submitted as a short paper.
● References (irrespective of complete paper or short paper submissions) do not count towards the page limit.
● Use single-spaced text with 12-point font and one-inch margins on four sides, printable on 8.5 x 11-inch paper.
● Submissions must be blinded. No author information should appear anywhere in the document.
● INFORMS or this workshop does not take ownership of paper copyrights.
● When uploading papers to the submission portal, the authors can indicate whether the paper’s main contributor is a student (so as to be considered for the best student paper award).

Organizing Committee:
Honorary Chairs
Olivia Sheng, Arizona State University
Alexander S. Tuzhilin, New York University

Conference Chairs
Weifeng Li, University of Georgia, weifeng.li@uga.edu
Haibing Lu, Santa Clara University, hlu@scu.edu
Yinghui (Catherine) Yang, UC Davis, yiyang@ucdavis.edu

Program Chairs
Jessica Clark, University of Maryland, jmclark@umd.edu
Yuanyang Liu, University of Tennessee, yliu191@utk.edu
Feng Mai, University of Iowa, feng-mai@uiowa.edu

Publicity Chairs
Benjamin M. Ampel, Georgia State University
Gene Moo Lee, University of British Columbia
Konstantina Valogianni, IE University
Xiang (Shawn) Wan, Santa Clara University
Renyu (Philip) Zhang, Chinese University of Hong Kong