[hpc-announce] Fwd: CFP [AI4S'24]: (Deadline extension) The 5th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications

[Murali Emani]

The submission deadline is now extended to August 15, 2024.

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[AI4S]: The 5th Workshop on Artificial Intelligence and Machine
Learning for Scientific Applications

To be held in conjunction with SC24
Monday, 18 November 2024, 9:00 am - 5.30 pm EST
Atlanta, GA, USA

Website: https://urldefense.us/v3/__https://ai4s.github.io/__;!!G_uCfscf7eWS!dj6w7HBdUEJGetkXCUxDOWIIsb8unaWaUk22ixVvNEtPK5wF98xaAlQnO5y53EG2WwdY1VZqjVtsnNRD0hXyG9aVuQ$ 
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Overview
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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 The workshop will be organized as a series of
plenary talks based on peer-reviewed paper submissions accompanied by
keynotes from distinguished researchers in the area and a panel
discussion. We encourage participation and submissions from
universities, industry, and DOE National Laboratories.

Artificial intelligence (AI)/machine learning (ML) is a game-changing
technology that has shown tremendous advantages and improvements in
algorithms, implementations, and applications. “AI for Science”
broadly refers to designing future methods and scientific
opportunities that use computational learning and machine
intelligence. This includes the development and application of AI
methods (e.g., machine learning, deep learning, statistical methods,
data analytics, automated control, and related areas). We have seen
many successful stories that AI methods are used to predict extreme
weather events, identify exoplanets in trillions of sky pixels,
accelerate numerical solvers in fluid simulation, design better
materials and processes, accelerate drug discovery, explore the
mysteries of the universe, and drive an array of scientific
discoveries. However, there are a number of problems remaining to be
studied to enhance the usability of AI/ML to scientific applications
by leveraging HPC systems. For example, how to systematically and
automatically apply AI/ML to scientific applications? How to
incorporate domain knowledge (e.g., conservation laws, invariants,
causality and symmetries) into AI/ML models? How to make the models
interpretable and robust for HPC? How to make AI/ML more approachable
to the HPC community? How to effectively utilize extreme scale HPC
systems and novel AI accelerators for AI/ML? Addressing the above
problems will bridge the gap between AI/ML and scientific applications
and enable wider employment of AI/ML in HPC.


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Call for Papers
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We solicit research papers in the following topic areas, but not be limited to:
- Innovative AI/ML models to analyze, accelerate, or improve
performance of scientific applications in terms of execution time and
simulation accuracy;
- Using HPC systems to accelerate AI/ML training and inferences on
large scientific data sets;
- Research challenges while using large-scale HPC systems for AI/ML;
- Innovative methods to incorporate complex constraints imposed by
physical principles to scientific applications;
- Workflow of applying AI/ML to scientific applications;
- Innovative methods to completely or partially replace first-order
computation with efficient AI/ML models;
- Tools and infrastructure to improve the usability of AI/ML to
scientific applications;
- Performance characterization and study on the possibility of using
AI/ML to specific scientific applications;
- Innovative methods to make AI models interpretable and robust for
scientific applications;
- Performance evaluation of emerging AI accelerators for scientific ML
workloads;
- Use of Generative AI to advance the scientific frontier;
- Tools and approaches to increase generative AI trustworthiness;
- Approaches to address scaling issues associated with Large Language Models.

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Submissions
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Authors are invited to submit manuscripts in English structured as
technical papers up to 6 pages 2-column pages (U.S. letter –
8.5″x11″), excluding the bibliography, using the ACM proceedings
template. Latex users, please use the “sigconf” option (use of the
“review” option is recommended but not required). 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://urldefense.us/v3/__https://submissions.supercomputing.org__;!!G_uCfscf7eWS!dj6w7HBdUEJGetkXCUxDOWIIsb8unaWaUk22ixVvNEtPK5wF98xaAlQnO5y53EG2WwdY1VZqjVtsnNRD0hUJW71fbQ$ , SC24 Workshop: AI4S'24:
Workshop on Artificial Intelligence and Machine Learning for
Scientific Applications".

The final papers will be published in the SC Workshops Proceedings.


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Important Dates:
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Submission: August 15, 2024 (AoE)
Notification of acceptance: September 6, 2024
Camera Ready: September 27, 2024
Workshop: November 18, 2024

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Organizers
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Gokcen Kestor, Pacific Northwest National Laboratory
Dong Li, University of California, Merced
Murali Krishna Emani, Argonne National Laboratory

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Program Committee
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Bilge Acun, Meta
Jeff Daily, AMD
Aleksandr Drozd, RIKEN
Akash Dutta, AMD
Rio Yokota, Tokyo Institute of Technology
Väinö Hatanpää, ANL
Kevin J. Barker, Pacific Northwest National Laboratory
Mehmet E Belviranli, Colorado School of Mines
Wesley Brewer, Oak Ridge National Laboratory
Wenqian Dong, Oregon State University
Aleksandr Drozd, RIKEN
Steven Farrell, Lawrence Berkeley National Lab
Roberto Gioiosa, Pacific Northwest National Lab
Jiajia Li, North Carolina State University
Zhengchun Liu, AWS
Jacques Pienaar, Google
Ben Ren, William and Mary
Kento Sato, RIKEN
Bin Ren, William&Mary
Dingwen Tao, Indiana University
Brian C. Van Essen, Lawrence Livermore National Laboratory
Venkatram Vishwanath, Argonne National Laboratory
Feiyi Wang, Oak Ridge National Laboratory
Bo Yuan, Rutgers University