Relevance

RELEVANCE TO KDD’25 AND AUDIENCE

The workshop is particularly relevant to KDD as it aligns with the conference’s commitment to showcasing pioneering datadriven research that bridges the gap between theoretical advances and real-world impact. As the field of LLMs rapidly evolves, its relevance to current challenges in data mining and AI is more apparent than ever. Many KDD participants, both from academia and industry, are actively engaged in exploring and pushing the boundaries of what LLMs can achieve. Notably, both KDD’23 and KDD’24 featured an LLM Day to highlight the exciting opportunities and challenges presented by advancements in LLMs. To provide a more interactive experience and cater to a diverse audience, we propose this workshop with a dedicated focus on the domains of science and society. By bringing together data scientists, AI researchers, domain experts, and policymakers, the workshop will spark engaging discussions about the latest LLM methodologies and their practical implementations. Through invited talks, panel discussions, and interactive sessions, such as oral and poster presentations, participants will gain firsthand insights into how LLMs are making a real difference across various fields, further encouraging interdisciplinary partnerships and innovation. This workshop is designed to drive progress at the crucial intersection of data mining, machine learning, and the challenges faced in science and society, which are core areas that KDD continually strives to advance.

The target audience for the SciSoc LLM Workshop includes a diverse group of stakeholders from academia, industry, and government agencies who are at the forefront of artificial intelligence research and applications. Specifically, the workshop aims to attract computer scientists, data scientists, AI researchers, and domain experts in fields such as healthcare, environmental science, public policy, and social sciences who are interested in the integration and impact of Large Language Models within their disciplines. Additionally, the event seeks to draw in policy makers and technology developers who are focused on ethical considerations and the practical deployment of AI technologies in real-world scenarios. This gathering will provide a unique opportunity for interdisciplinary exchange and collaboration, aiming to foster innovations that drive scientific discovery and address pivotal societal challenges through the advanced capabilities of LLMs.

PLAN TO ATTRACT QUALITY SUBMISSIONS

To attract high-quality submissions, we plan to implement a multifaceted approach that leverages both traditional and innovative outreach strategies. We will initiate a targeted call for papers through well-established mailing lists in the fields of AI, machine learning, and data science, including those of the ACM and IEEE, as well as subscribers of relevant AI and data science newsletters. Additional outreach will occur via social media channels such as LinkedIn, X, WeChat, and Threads, as well as through academic networks like ResearchGate, to reach a broad spectrum of researchers and practitioners. To maximize exposure for the SciSoc LLM Workshop, we also plan to engage directly with university departments and promote through global forums to ensure diverse and international participation.

To encourage participation from top-tier researchers, leading experts in the field will be invited to contribute keynotes and participate in panel discussions. Additionally, collaboration with relevant journals and publications for special issues featuring selected papers from the workshop will provide further incentives for submitting high-caliber work. Recognizing the interdisciplinary nature of the workshop, we will also reach out to professionals in sectors such as healthcare, environmental science, and public policy, ensuring a rich variety of perspectives and innovative applications of LLMs.

The proposed workshop complements and extends the dialogue from several related workshops previously or concurrently held at major conferences such as KDD, NeurIPS, ICML, and AAAI. Notably, workshops like “Data Science for Social Good,” “Machine Learning for Health (ML4H),” and “AI for Science” have similarly explored the intersection of AI technologies and their societal impacts. Additionally, the “Tackling Climate Change with Machine Learning” workshop and the “Machine Learning for Policy and Economics” workshop at NeurIPS have addressed specific applications of data science in critical societal domains. Our workshop aims to specifically harness the capabilities of LLMs, building on these discussions with a focus on their unique potential to revolutionize traditional research methodologies and societal applications. By situating itself within this established framework of scientific discourse, the SciSoc LLM Workshop seeks to provide a novel platform for advancing these conversations with a fresh focus on LLM-driven innovations.

TENTATIVE PROGRAM COMMITTEE LIST

We have connected with a diverse group of scholars interested in serving as program committee members (reviewers) for our workshop. These individuals come from both academia and industry, and our selection includes those from underrepresented backgrounds in STEM. The reviewers will include faculty members and senior industrial researchers:

  • Jamell Dacon, Morgan State University
  • Meng Jiang, University of Notre Dame
  • Carl Yang, Emory University
  • Zhichun Guo, Emory University
  • Shengpu Tang, Emory University
  • Yuxuan Liang, HKUST
  • Ming Jin, Monash University
  • Yao Ma, RPI
  • Xu Han, University of Arizona
  • Tyler Derr, Vanderbilt University
  • Wei Jin, Emory University
  • Lu Cheng, University of Illinois at Chicago
  • Wenpeng Yin, Penn State
  • Xianfeng Tang, Amazon
  • Qingsong Wen, Squirrel AI
  • Danai Koutra, University of Michigan
  • B. Aditya Prakash, Georgia Tech
  • Yan Liu, University of Southern California

Moreover, we will also invite senior PhD students from Emory University, University of Illinois Chicago, Georgia Tech, Penn State University, University of Michigan, University of Illinois Urbana-Champaign, UNC Chapel Hill, and other institutions.