TPC will be providing the following full- and half-day tutorials on Monday, July 28 and Tuesday, July 29:
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 Tuesday, July 29. Download the sponsorship prospectus here.
Tutorials are open to all conference attendees, for an additional fee.
(case studies)
(frameworks/tools)
(frameworks/tools)
(advanced topics)
(advanced topics)
Vivek Natarajan, Google DeepMind
This talk highlights general purpose AI systems designed at Google to democratize medical expertise and accelerate scientific discovery. We will first take a look at the AI co-scientist, built to accelerate scientific breakthroughs by assisting scientists in generating novel hypotheses and aiding experimental design. This system has yielded validated results in areas like genetic discovery, drug repurposing, target discovery, and understanding antimicrobial resistance. Secondly, we will examine the AI co-physician, AMIE, developed to make medical expertise universally accessible through capabilities such as advanced diagnostic dialogue. In simulations, AMIE outperformed primary care physicians on multiple clinical evaluation axes and showed promise as an assistive tool, with ongoing real-world validations. Together, these AI initiatives demonstrate the potential to transform scientific research and care delivery.
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 tutorial will be led by Neeraj Kumar (PNNL), Samantika Sury (HPE), Laura Morselli (CINECA), and Prasanna Balaprakash (ORNL).
Plenary session with all Tutorial and Hackathon participants:
Foundations in AI for Science
Case Studies and Emerging Frontiers in AI for Science
Parallelization Strategies for Large-Scale Pre-Training
Fine-Tuning Techniques: From Theory to Practice
Profiling AI Workloads with PARAVER
Building RAG-based Workflows OR AI Agents
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.
Plenary session with all Tutorial and Hackathon participants: Foundations in LLMs for Science
Use cases and basic evaluation techniques
Advanced evaluation techniques
Hands on
This is a hands-on tutorial designed to equip researchers, students, engineers, and scientific leaders with practical skills for the use of AI models and systems to accelerate planning, inquiry, coding, and other common tasks.
This tutorial will demonstrate how computational scientists can effectively harness AI-powered tools across the research lifecycle to increase their productivity. Attendees will learn to generate novel research ideas and hypotheses using agents for Deep Research and Idea Generation. We then cover structuring comprehensive research plans with AI assistance.
For implementation, attendees will see how to efficiently port, develop, and optimize code using tools like Google’s Gemini Code Assist and CLI, alongside advanced optimizers such as AlphaEvolve. While this tutorial will use Google technologies for the examples (and attendees will be given accounts to access them), the core principles and strategies are designed to be portable, enabling scientists to effectively use any comparable AI tool in their own scientific endeavors.
This tutorial will be conducted by Jay Boisseau, Advanced Computing Strategist at Google.
LLM Refresher + Deep Research & Idea Generation
Coding Faster & Better (Usually)
+
AI-enabled Science Applications
are open to all conference attendees, for an additional fee.
TPC25 is produced by TCI Media, home of the Wire Publications:
© 2025 TCI Media.
TPC25 is produced by TCI Media, home of the Wire Publications:
© 2025 TCI Media.