Workshop Program

AI4S 2024 will be held on Monday, November 18. The schedule is provided below. All times are in EST.

9am	Welcome and Intro
9am - 10am EST	AI4S Featured Speaker: Torsten Hoefler Professor of Computer Science at ETH Zurich
10am - 10:30am EST	Morning Break
10:30am - 11:30am EST	AI4S Featured Speaker: Satoshi Matsuoka Director of RIKEN Center for Computational Science
11:30am - 11:50am EST	Machine Learning Aboard the ADAPT Gamma-Ray Telescope Ye Htet, Marion Sudvarg, Andrew Butzel, Jeremy Buhler, Roger Chamberlain, and James Buckley. Session chair: Dong Li
11:50am - 12:10pm EST	A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics Junqi Yin, Siming Liang, Siyan Liu, Feng Bao, Hristo G. Chipilski, Dan Lu, and Guannan Zhang Session chair: Dong Li
12:10pm - 12:30pm EST	ChatBLAS: The First AI-Generated and Portable BLAS Library Pedro Valero-Lara, William Godoy, Keita Teranishi, Prasanna Balaprakash, and Jeffrey Vetter. Session chair: Dong Li
12:30pm - 2pm EST	Lunch Break
2pm - 2:20pm EST	Echo State Networks: A Non-Intrusive Approach to Anomaly Detection in Manufacturing Kendric Hood Session chair: Gokcen Kestor
2:20pm - 2:40pm EST	MelissaDL x Breed: Towards Data-Efficient On-Line Supervised Training of Multi-Parametric Surrogates with Active Learning Sofya Dymchenko, Abhishek Purandare, and Bruno Raffin Session chair: Gokcen Kestor
2:40pm - 3pm EST	Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence Mohammad Atif, Pulkit Dubey, Pratik P. Aghor, Vanessa Lopez-Marrero, Tao Zhang, Abdullah Sharfuddin, Kwangmin Yu, Fan Yang, Foluso Ladeinde, Yangang Liu, Meifeng Lin, and Lingda Li Session chair: Gokcen Kestor
3pm - 3:30pm EST	Afternoon Break
3:30pm - 3:50pm EST	ChatVis: Automating Scientific Visualization with a Large Language Model Tanwi Mallick, Orcun Yildiz, David Lenz, and Tom Peterka Session chair: Murali Krishna Emani
3:50pm - 4:10pm EST	A Comparative Survey: Reusing Small Pre-Trained Models for Efficient Large Model Training Dhroov Pandey, Jonah Ghebremichael, Zongqing Qi, and Tong Shu Session chair: Murali Krishna Emani
4:10pm - 4:30pm EST	Enhancing Electron Microscopy Image Classification Using Data Augmentation Jordan Alan Welsman, Gunther H. Weber, Oluwamayowa O. Amusat, Anna Giannakou, and Lavanya Ramakrishnan. Session chair: Murali Krishna Emani
4:30pm - 4:45pm EST	SciTrust: Evaluating the Trustworthiness of Large Language Models for Science Emily Herron, Junqi Yin, and Feiyi Wang Session chair: Wenqian Dong
4:45pm - 5pm EST	AI Surrogate Model for Distributed Computing Workloads David K. Park, Yihui Ren, Ozgur O. Kilic, Tatiana Korchuganova, Sairam Sri Vatsavai, Joseph Boudreau, Tasnuva Chowdhury, Shengyu Feng, Raees A. Khan, Jaehyung Kim, Scott Klasky, Tadashi Maeno, Paul Nilsson, Verena Ingrid Martinez Outschoorn, Norbert Podhorszki, Frédéric Suter, Wei Yang, Yiming Yang, Shinjae Yoo, Alexei Klimentov, and Adolfy Hoisie Session chair: Wenqian Dong
5pm - 5:15pm EST	Entropy-Driven Optimal Sub-Sampling of Fluid Dynamics for Developing Machine-Learned Surrogates Wesley Brewer, Daniel Martinez, Muralikrishnan Gopalakrishnan Meena, Aditya KashiKatarzyna Borowiec, Siyan Liu, Christopher Pilmaier, Greg Burgreen, and Shanti Bhushan Session chair: Wenqian Dong
5:15pm - 5:30pm EST	AstroMLab 2: Benchmarking Specialised LLMs for Astronomy Rui Pan, Tuan Dung Nguyen, Alberto Accomazzi, Tirthankar Ghosal, and Yuan-Sen Ting Session chair: Wenqian Dong
5:30pm EST	Closing Wenqian Dong, Oregon State University

Call for Papers

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.

Topics will include but will 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.

Submission

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 IEEE 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, 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.

Important Dates