[IISE Faculty List] INFORMS Journal on Data Science Call for Paper

[Ding, Yu]

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INFORMS Journal on Data Science Special Issue on GenAI, Foundation Models, and Deep Learning with Applications to Business Analytics<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpubsonline.informs.org%2Fpage%2Fijds%2Fcalls-for-papers&data=05%7C02%7Ciefac.list%40mailman.clemson.edu%7C638ea6e5cc3841a06bae08dd8bd0abd8%7C0c9bf8f6ccad4b87818d49026938aa97%7C0%7C0%7C638820450817838068%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=ixKoD0%2BiNqsfPnVz9vjj23DZHeMUC2Y58vbgpigbh%2B8%3D&reserved=0>

The rapid advancement of generative AI (GenAI), foundation models (large language models in particular), and deep learning more broadly has ushered in a new era of data science, fundamentally transforming how organizations and individuals approach problem-solving and analytics. These tools can significantly enhance the capabilities of organizations and individuals in decision-making, problem-solving, and analytics. These technologies are not only pushing the boundaries of what is possible in data science but also presenting remarkable opportunities and significant research challenges for the INFORMS community. This special issue seeks to explore the transformative potential of GenAI, foundation models, large language models (LLMs) and deep learning across three key areas: (1) Innovative applications in data science. (2) Understanding GenAI, LLMs, and Deep Learning in a practical context. (3) Societal impact and policy implications.

Although the guest editors are all from business schools, papers from engineering schools are also welcome.

Submission
To be considered for the special issue on Generative AI, Foundation Models, and Deep Learning with Applications to Business Analytics, submit your manuscript online via
https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmc.manuscriptcentral.com%2Fijds&data=05%7C02%7Ciefac.list%40mailman.clemson.edu%7C638ea6e5cc3841a06bae08dd8bd0abd8%7C0c9bf8f6ccad4b87818d49026938aa97%7C0%7C0%7C638820450817869906%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Zrd8fuBmYVhmRMhv3VhJ%2BdQjXEZ9yWrZl4SipHrQr4M%3D&reserved=0. Select "Virtual Special Issue om GenAI etc. for Business Analytics" as the manuscript type in Step 1. Manuscripts will be assigned to one of the guest editors for this issue.

The virtual special issue aims to provide timely outlets for innovative, cutting-edge research on the aforementioned topics and beyond. A paper submitted to the virtual special issue will be processed right away, and accepted papers will be published in regular issues without delay. As such, authors are encouraged to submit as soon as they are ready. This virtual special issue will be an online collection of all these articles tied together under a unifying editorial article for greater impact and outreach.

Important Timelines

  *   Submission deadline September 1, 2025. Manuscripts will be reviewed as they are received.
  *   First round of decision by December 1, 2025.
  *   Guest editors are committed to completing revision review within 60 days.
  *   Maximum two rounds of revisions.
  *   All final decisions are expected to be made by August 1, 2026.

Guest Editors

  *   Ahmed Abbasi, University of Notre Dame
  *   Ningyuan Chen, University of Toronto
  *   Xiaocheng Li, Imperial College London
  *   Xiao Liu, New York University


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