# DS 2025 : INFORMS Workshop on Data Science 2025 Taking place in United States, Conference was concluded on October 25, 2025. Primary subject areas for this edition include Artificial Intelligence and data science. **Type**: Conference **Status**: Expired **Verified On**: 9th May, 2026 --- ## ⏱ Critical Deadline > *No submission deadline specified* --- ## 📍 Event Information - **Mode**: In Person - **Location**: United States - **Field**: Interdisciplinary - **Date**: October 25, 2025 - **Official Website**: [https://sites.google.com/view/data-science-2025](https://sites.google.com/view/data-science-2025?utm_source=callforpaper.org) --- ## 📝 Call for Papers Description The 9th INFORMS Workshop on Data Science (DS 2025) 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:
- Adaptation of emerging deep learning techniques, e.g., transformers, and graph embedding approaches for targeted business applications
- Computational Approaches for Measuring and Enhancing Trust in AI Systems
- Algorithmic Fairness, Bias Mitigation, and Equity in AI
- AI-Powered Knowledge Graphs and Reasoning.
- Generative AI and its various impacts on individuals, organizations, and societies
- Modifications (e.g., domain adaptation, RAG, fine-tuning) and applications of generative AI and large language models tailored to the unique needs of an application area
- Implications of the use of AI, including generative AI and large language models, in real-world settings
- Ethical AI frameworks and guidelines for responsible AI development and deployment
- AI for environmental sustainability and climate change
- AI in digital marketing and consumer behavior analysis
- Human-AI collaboration and augmented intelligence
- Explainable AI (XAI) and interpretability of AI models
- 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
- Visual analytics for business data in image and video formats
- Mobile analytics and spatial-temporal data mining
- 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:

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
- Panos Adamopoulos, Emory University, Panagiotis.Adamopoulos@emory.edu
- Konstantin Bauman, Temple University, kbauman@temple.edu

Program Chairs
- Yan Leng, University of Texas - Austin, yan.leng@mccombs.utexas.edu
- Pan Li, Georgia Tech, pan.li@scheller.gatech.edu
- Weifeng Li, University of Georgia, weifeng.li@uga.edu

Publicity Chairs
- Yingfei Wang, University of Washington, yingfei@uw.edu
- Jing Wang, HKUST, jwang@ust.hk
- Konstantina Valogianni, IE Business School, konstantina.valogianni@ie.edu
- Moshe Unger, Tel Aviv University, mosheunger@tauex.tau.ac.il
- Gene Moo Lee, UBC, gene.lee@sauder.ubc.ca

--- ## 🏷 Taxonomy & Topics - **Primary Category**: N/A - **Research Fields**: - Artificial Intelligence - data science --- ## 🧭 Agent Instructions - **Standard Link**: `https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925` - **Markdown Link**: `https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925.md` - **PDF Version**: `https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925.pdf` --- ## 🎓 Academic Citations & Bibliographic Records Use these pre-formatted snippets to cite this Call for Papers in publications or reference managers: ### Plain Text ```text DS 2025 : INFORMS Workshop on Data Science 2025. United States, October 25, 2025. Available at: https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925. Accessed: July 21, 2026. ``` ### APA Style 7th edition ```text DS 2025 : INFORMS Workshop on Data Science 2025. (2025, October 25). United States. https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925 ``` ### IEEE ```text "DS 2025 : INFORMS Workshop on Data Science 2025," United States, Oct. 25, 2025. [Online]. Available: https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925. [Accessed: July 21, 2026]. ``` ### BibTeX ```bibtex @misc{ds20252025cfp, title = {DS 2025 : INFORMS Workshop on Data Science 2025}, year = {2025}, month = oct, address = {United States}, url = {https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925}, note = {Call for Papers. Accessed: 2026-07-21} } ``` ### RIS (Reference Manager, EndNote) ```ris TY - CONF TI - DS 2025 : INFORMS Workshop on Data Science 2025 PY - 2025 DA - 2025/10/25 CY - United States UR - https://callforpaper.org/cfp/ds-2025-informs-workshop-on-data-science-2025_3925 N1 - Call for Papers. Accessed: 2026-07-21 ER - ```