TPC will be providing the following full- and half-day tutorials on Sunday, May 31 and Monday, June 1:
These tutorials have been refined over the past 18 months, including the introductory “AI for Science” tutorial that has been presented to hundreds of people at Supercomputing Asia in February 2024, the TPC European Kickoff Workshop in June 2024, the University of Michigan’s annual Conference on Foundation Models and AI Agents for Science, and numerous local training events.
Sponsors are welcome to provide appropriate half-day tutorials on a subject that would be of interest to TPC members the morning of Monday, June 1. Download the sponsorship prospectus here.
Tutorials are open to all conference attendees, for an additional fee.
This is a hands-on tutorial designed to equip researchers with practical skills and conceptual grounding in the application of large-scale AI models to scientific challenges. The program covers key components of the AI model lifecycle — from distributed strategies for pre-training generative models to fine-tuning techniques for domain-specific tasks using models like LLAMA-70B and Stable Diffusion.
Participants will also learn to analyze and optimize performance through workload profiling with PARAVER, and to build intelligent scientific workflows using Retrieval-Augmented Generation (RAG) and agent-based approaches. The tutorial concludes with real-world case studies across disciplines — biology, climate, physics, chemistry — highlighting lessons learned from deployment and emerging trends such as simulation models and neural-symbolic systems.
Participants will develop a practical understanding of large-scale AI model development, including:
Attendees will come away with exposure to real-world scientific applications and current research frontiers in AI for science.
This is a hands-on tutorial designed to equip researchers with practical skills and conceptual grounding in the application of LLMs to scientific challenges. Large Language Models (LLMs) are becoming capable of solving complex problems while presenting the opportunity to leverage them for scientific applications. However, even the most sophisticated models can struggle with simple reasoning tasks and make mistakes.
This tutorial focuses on best practices for evaluating LLMs for science applications. It guides participants through methods and techniques for testing LLMs at basic and intermediate levels. It starts with the fundamentals of LLM design, development, application, and evaluation while focusing on scientific application. Participants will also learn various complementary methods to rigorously evaluate LLM responses in benchmarks and end-to-end scenario settings. The tutorial features a hands-on session where participants use LLMs to solve provided problems.
This tutorial will be conducted by Franck Cappello, R&D Lead, Senior Computer Scientist at Argonne National Laboratory and Sandeep Madireddy, Computer Scientist and AI Researcher at Argonne National Laboratory.
This hands-on tutorial will equip computational scientists, engineers, developers, and students with practical skills for using AI models, tools, and agentic systems for maximum productivity, innovation, and discovery. The tutorial will begin by providing a foundation for understanding both predictive and generative AI methods, including how to minimize errors and to increase accuracy and useful results. The tutorial will then cover the powerful capabilities of LLMs and multi-modal models, with demos and hands-on labs. The majority of the workshop will then show attendees how AI technologies can augment phases from discovery and innovation from start to finish: deep literature research, ideation/hypothesis generation, research/development planning, application prototyping and development, code optimization, surrogate creation, and data analysis. The emphasis for every phase will be how AI technologies can assist and empower (not replace), and which tools are most useful for each task (and why).
Tutorial examples and labs will leverage the latest production and research/prototype Google technologies — Gemini, NotebookLM, Gemini CLI, Code Assist, Antigravity, AlphaEvolve, Co-Scientist, and others — that are available at time of this workshop. However, the core principles and strategies are designed to be portable, enabling scientists to effectively use any comparable AI models and tools in their own endeavors (and even in most of the labs). Multiple AI science applications (WeatherNext, AlphaFold 3, AlphaGenome, etc.) developed by Google DeepMind will be used to show the capabilities of AI-powered scientific discovery.
Agentic systems, in which autonomous agents collaborate to solve complex problems, are emerging as a transformative methodology in AI. However, adapting agentic architectures to scientific cyberinfrastructure — spanning HPC systems, experimental facilities, and federated data repositories — introduces new technical challenges. In this half-day tutorial, we introduce participants to the design, deployment, and management of scalable agentic systems for scientific discovery. We will present Academy, a Python-based middleware platform built to support agentic workflows across heterogeneous research environments.
Participants will learn core agentic system concepts, including asynchronous execution models, stateful agent orchestration, and dynamic resource management. A guided hands-on session will help attendees build and launch their own agentic systems. This tutorial is designed for researchers, developers, and cyberinfrastructure professionals interested in advancing AI-driven science with next-generation autonomous systems.
This tutorial will be conducted by Kyle Chard, Research Associate Professor at University of Chicago, Ian Foster, Data Science and Learning Division Director at Argonne National Laboratory, and Alok Kamatar of the University of Chicago.
are open to all conference attendees, for an additional fee.
TPC26 is produced by TCI Media, home of the Wire Publications:
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TPC25 is produced by TCI Media, home of the Wire Publications:
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