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AI4S:The 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications

Workshop Overview

The workshop will be held in conjunction with SC 25: The International Conference for High Performance Computing, Networking, Storage and Analysis located in St. Louis, MO on November 16 - 21. The AI4S workshop will be held on Monday, November 17, 9 AM-5.30 PM Central Daylight Time (CDT). The purpose of this workshop is to bring together computer scientists and domain scientists from academia, government, and industry to share recent advances in the use of AI/ML in various scientific applications, introduce new scientific application problems to the broader community, and stimulate tools and infrastructures not only to support the application of AI/ML in scientific applications but also effectively utilize existing and future HPC systems for AI/ML-based scientific applications.




Featured Speakers

Speaker 1

Dr. Tom Gibbs

NVIDIA

Tom Gibbs leads software development strategies at NVIDIA to advance research in Grand Challenge Science Problems, focusing on the convergence of AI, advanced classical simulation methods, and experimental data acquisition for scientific applications.

Speaker 2

Dr. Hatem Ltaief

Principal Research Scientist, KAUST

Dr. Hatem Ltaief is a Principal Research Scientist at KAUST. He works on mixed-precision algorithms and performance optimizations to help HPC scientific applications address the exascale challenges. His innovations have been recognized with the ACM Gordon Bell Prize (shared) for Climate Modeling in 2024.

Call for Papers

Artificial Intelligence (AI) and Machine Learning (ML) are transforming scientific discovery, driving breakthroughs in climate modeling, materials discovery, astrophysics, drug development, and many other domains. AI for Science (AI4S) focuses on developing and applying computational learning and machine intelligence to accelerate innovation in scientific research. Recent years have witnessed AI-driven advances that have reshaped the landscape of scientific research. AI methods have been successfully applied to predict extreme weather events with greater accuracy, identify exoplanets among trillions of sky pixels, accelerate numerical solvers for fluid dynamics and physics-based simulations, design novel materials and chemical processes, improve drug discovery pipelines, and help uncover fundamental insights into the universe. At the same time, generative AI is revolutionizing not only scientific computing but also broader societal applications. However, despite these advancements, several fundamental challenges remain in effectively integrating AI/ML into scientific workflows, particularly when leveraging high-performance computing (HPC) resources. One of the most pressing questions is how to systematically and automatically apply AI/ML techniques to complex scientific applications while ensuring reliability, interpretability, and efficiency. The integration of domain knowledge, such as conservation laws, invariants, causality, and symmetries, remains an open problem that requires deeper exploration. Additionally, enhancing the robustness of AI models for HPC environments and making them interpretable for scientific analysis is a crucial step toward broader adoption. Foundation models designed for scientific applications need further refinement to meet domain-specific requirements, and significant efforts are needed to reduce the energy cost of large-scale AI training. Ensuring that AI/ML frameworks are approachable and efficient for the broader HPC community is another important challenge, along with effectively utilizing extreme-scale HPC systems and emerging AI accelerators to unlock new scientific possibilities.

Topics will include but will not be limited to:

  • Advancing scientific discovery and development workflows through generative AI models;
  • Strategies for reducing the energy consumption of AI model training and inference;
  • Investigating the impact of quantization and reduced precision techniques on the accuracy and correctness of AI-driven scientific applications;
  • Novel AI/ML approaches for improving execution time, efficiency, and simulation accuracy;
  • Characterizing the impact, accuracy, and effectiveness of AI/ML for scientific workloads;
  • Leveraging HPC systems to accelerate training and inference of AI/ML models on large-scale scientific datasets;
  • Addressing scalability, optimization, and efficiency issues when deploying AI/ML on extreme-scale HPC platforms;
  • Methods for integrating domain knowledge, including physical laws and constraints, into AI-driven scientific modeling;
  • Replacing or augmenting traditional numerical methods with AI/ML models for computational efficiency;
  • Tools, frameworks, and infrastructure to enhance the usability and accessibility of AI in scientific applications;
  • Methods to improve interpretability, explainability, and robustness of AI models for critical scientific applications;
  • Performance evaluation and benchmarking of emerging AI accelerators (e.g., GPUs, TPUs, FPGAs, neuromorphic computing) for scientific workloads;
  • The AI4S workshop provides a platform for experts from academia, government, and industry to explore these challenges, share recent advances, and discuss emerging opportunities in AI-driven scientific discovery. The workshop aims to bridge the gap between AI research and scientific applications by bringing together computer scientists, domain experts, and HPC practitioners to exchange ideas and foster collaboration. By showcasing cutting-edge AI/ML advancements, identifying key challenges in deploying AI at scale, and facilitating discussions on the future of AI in science, this workshop will serve as a catalyst for accelerating AI adoption in scientific research.

    Submission

    Authors are invited to submit manuscripts in English structured as technical papers up to 6 pages 2-column pages, min 5 pages (U.S. letter – 8.5″x11″), excluding the bibliography, using the ACM proceedings template that is available at here. The manuscripts are single-blind. Word authors can use the “Interim Layout”. Submissions not conforming to these guidelines may be returned without review.
    All manuscripts will be peer-reviewed and judged on correctness, originality, technical strength, and significance, quality of presentation, and interest and relevance to the workshop attendees. Submitted papers must represent original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines will be rejected without review and further action may be taken, including (but not limited to) notifications sent to the heads of the institutions of the authors and sponsors of the conference. Submissions received after the due date, exceeding length limit, or not appropriately structured may also not be considered. At least one author of an accepted paper must register for and attend the workshop. Authors may contact the workshop organizers for more information.
    Papers should be submitted electronically at: https://submissions.supercomputing.org, SC25 Workshop: AI4S'25: Workshop on Artificial Intelligence and Machine Learning for Scientific Applications".
    The final papers will be published in the SC Workshops Proceedings.

    Important Dates

    Submission Deadline

    August 8 (11:59pm, AOE), 2025

    Notification of acceptance

    September 5, 2025

    Camera Ready

    September 26, 2025

    Organizers

  • Gokcen Kestor, Pacific Northwest National Laboratory
  • Dong Li, University of California, Merced
  • Murali Krishna Emani, Argonne National Laboratory
  • Technical Program Committee

    TBA
  • Deborah Bard, National Energy Research Scientific Computing Center (NERSC)
  • Kevin J. Barker, Pacific Northwest National Laboratory
  • Mehmet E Belviranli, Colorado School of Mines
  • Wesley Brewer, Oak Ridge National Laboratory
  • Marc Casas Guix, Barcelona Supercomputing Center (BSC), Polytechnic University of Catalonia
  • Wenqian Dong, Oregon State University
  • Steven Farrell, Lawrence Berkeley National Lab
  • Roberto Gioiosa, Pacific Northwest National Lab
  • Yao Fehlis, AMD Research
  • Jiajia Li, North Carolina State University
  • Jie Ren, William & Mary
  • Shashank Subramanian, Lawrence Berkeley National Laboratory (LBNL)
  • Venkatram Vishwanath, Argonne National Laboratory
  • Feiyi Wang, Oak Ridge National Laboratory
  • Zhen Xie, State University of New York at Binghamton
  • Junqi Yin, Oak Ridge National Laboratory
  • Rio Yokota, Institute of Science Tokyo
  • Bo Yuan, Rutgers University