Advanced Topics in AI for Science: A Student Training Series

Oct 14, 2025, 3:00 PM → Nov 11, 2025, 4:30 PM America/Chicago

Zoom (Virtual Training Series)

Building on ALCF’s series Intro to AI-Driven Science on Supercomputers, we are hosting a series of hands-on courses that will expand upon advanced topics in AI for science. Attendees will deepen their understanding of applying AI at scale, learn about coupling science simulations with AI, dig into inference workflows, and explore how AI accelerators are enabling for AI for Science.

Pre-requisites

This training series is aimed at undergraduate and graduate students enrolled at U.S. universities. Attendees are expected to:

·       Have foundational knowledge of Python

·       Have either attended the ALCF Intro to AI-Driven Science on Supercomputers series or have familiarity with the topics covered in the series

o   Topics include foundational concepts in parallel computing, neural networks, large-language models, prompt engineering, AI accelerators

o   Those without this background knowledge are welcome to look through the Intro to AI videos and materials before the start of the series.

Machine Access

• To access ALCF machines, do the following by Sept 30, 2025:

  • Current ALCF Account: submit a request to join 'ALCFAITP' project at: https://accounts.alcf.anl.gov/#/joinProject​

  • No ALCF Account: request one at: https://accounts.alcf.anl.gov/#/accountRequest. When prompted for project name, select 'ALCFAITP'.​

  • Inactive ALCF Account: submit a reactivation request at: https://accounts.alcf.anl.gov/#/accountReactivate. When prompted for project name, select 'ALCFAITP'.

 

You must use your institution email address at signup.

Account requests submitted after Sept 30 cannot be guaranteed ALCF accounts.

Due to DOE restrictions, participants not at US-based institutions cannot be guaranteed ALCF accounts. Participants without ALCF accounts are still welcome to attend sessions.

Event Website

ALCF AI for Science Training Series

Event Dates

The virtual workshop series will take place Tuesdays from 3:00pm - 4:30pm CT, October 14 – November 11, 2025.

Tue, October 14

3:00 PM → 4:30 PM Session 1: Intro to Artificial Intelligence on Supercomputers

Intro to AI Series: Session 1
Trainees will learn the basics of supercomputers and high-performance computing. They will be introduced to parallel programming and the fundamentals of training AI models on supercomputers.

Lecturer
Huihuo Zheng is a Computer Scientist at the Argonne Leadership Computing Facility. His areas of interests include data management and parallel I/O, large-scale distributed training. He applies high performance computing and deep learning to various domain sciences, such as physics, chemistry and material sciences. He also co-lead the MLPerf Storage Benchmarking group to develop benchmark suites for evaluating the performance of storage system for AI applications. 

AI for Science Talk Speaker
Troy Arcomano is a postdoctoral fellow at Argonne National Lab working on machine learning applications for weather and climate in the EVS division. During his time at ANL, he was the Argonne lead for several projects including a large collaboration to create a state-of-the-art foundation model for weather prediction. Troy received his PhD at Texas A&M University where he worked on developing machine learning applications for weather forecasting and investigated how machine learning could be used to improve climate models. He'll be speaking about the AI revolution for Weather and Climate.

Conveners: Huihuo Zheng (LCF), Troy Arcomano (EVS)

Tue, October 21

3:00 PM → 4:30 PM Session 2: Introduction to Neural Networks

Intro to AI Series: Session 2
Trainees will learn the basics of neural networks, opening up the black box of machine learning by building out by-hand networks for linear regression to increase the understanding of the math that goes into machine learning methods.

Lecturer
Marieme Ngom is an Assistant Computer Scientist at the Argonne Leadership Computing Facility. Her research interests include probabilistic machine learning, high-performance computing, and dynamical systems modeling with applications in chemical engineering and material sciences. Ngom received her Ph.D. in mathematics from the University of Illinois at Chicago (UIC) in 2019 under the supervision of Prof. David Nicholls. Marieme holds an MSc in mathematics from the University of Paris-Saclay (formerly Paris XI), an MSc in computer science from the National Polytechnic Institute of Toulouse, and an MEng in computer science and applied mathematics from the École nationale supérieure d’électrotechnique, d’électronique, d’informatique, d’hydraulique et des télécommunications (ENSEEIHT) in Toulouse.

AI for Science Talk Speaker
Nina Andrejevic is a Maria Goeppert Mayer Fellow at Argonne National Laboratory. Her research focuses on developing physics-aware machine learning models to assist the analysis and interpretation of materials characterization data. She received her B.S. in Engineering Physics from Cornell University and her Ph.D. in Materials Science and Engineering from Massachusetts Institute of Technology. Alongside her research, she is also enthusiastic about science communication through teaching and scientific data visualization. Her talk will cover Advancing materials characterization through physics-guided machine learning.

Conveners: Marieme Ngom (LCF), Nina Andrejevic (MSD)

Tue, October 28

3:00 PM → 4:30 PM Session 3: Advanced Topics in Neural Networks

Intro to AI Series: Session 3
Trainees will learn advanced topics in convolutional neural networks, such as deep, residual, variational, and adversarial networks

Lecturer
Bethany Lusch is a Computer Scientist in the data science group at the Argonne Leadership Computing Facility at Argonne National Lab. Her research expertise includes developing methods and tools to integrate AI with science, especially for dynamical systems and PDE-based simulations. Her recent work includes developing machine-learning emulators to replace expensive parts of simulations, such as computational fluid dynamics simulations of engines and climate simulations. She is also working on methods that incorporate domain knowledge in machine learning, representation learning, and using machine learning to analyze supercomputer logs. She holds a Ph.D. and MS in applied mathematics from the University of Washington and a BS in mathematics from the University of Notre Dame.

AI for Science Talk Speaker
Nesar Ramachandra is a cosmologist with interests in the dynamics of large-scale structure formation; he is also working on the implementation of state of the art statistical and machine learning methods for cosmological data analysis and fast prediction tools (emulators) as part of the SciDAC-4 project led by CPAC. In his talk, he will speak about AI for Cosmology.

Conveners: Bethany Lusch (LCF), Nesar Ramachandra (CPS)

Tue, November 4

1:55 PM → 3:25 PM Session 4: Introduction to Large Language Models (LLM)

Intro to AI Series: Session 4
Trainees will learn how computer models generate and comprehend natural language. The session will cover the architecture of large language models, input tokenization, and practical applications.

Lecturer
Archit Vasan is a postdoctoral appointee in the Argonne Leadership Computing Facility with a background in computational biophysics. His research interests at ALCF involve the discovery of cancer drugs using machine Learning coupled to exascale computing. Archit received a BA in Physics and Mathematics from Austin College in 2016. He then received his PhD in Biophysics from the University of Illinois at Urbana-Champaign in 2023 under the guidance of Dr. Emad Tajkhorshid.

AI for Science Talk Speaker
Nicola Ferrier is a senior computer scientist as part of the Mathematics and Computer Science division at Argonne National Laboratory. Ferrier's research interests are in the use of computer vision (digital images) to control robots, machinery, and devices, with applications as diverse as medical systems, manufacturing, and projects that facilitate ​“scientific discovery” (such as her recent project using machine vision and robotics for plant phenotype studies). She will be speaking on  AI @ Edge.

Conveners: Archit Vasan (LCF), Nicola Ferrier (MCS)

Tue, November 11

2:00 PM → 3:10 PM Session 5: Intro to AI Series: Introduction to LLM Prompt Engineering

Intro to AI Series: Session 5

Trainees will learn about the nuanced craft of prompt engineering, exploring the roles of clarity, relevance, specificity, and the strategic balance between detail and conciseness when designing prompts for Large Language Models. Trainees will also learn about Research Augmented Generation (RAG), a method that enhances LLM performance by integrating external data through retrieval tools.

Lecturers

Shilpika is a Postdoctoral Appointee at the Argonne Leadership Computing Facility. Her research focuses on data visualization and analysis of high-performance computing systems, including visualization and interpretation of AI for science to enable informed decision-making in AI workflows. Shilpika obtained an MS in Computer Science in 2016 from Loyola University Chicago and a Ph.D. in Computer Science in 2023 from the University of California Davis.

Archit Vasan is a postdoctoral appointee in the Argonne Leadership Computing Facility with a background in computational biophysics. His research interests at ALCF involve the discovery of cancer drugs using machine Learning coupled to exascale computing. Archit received a BA in Physics and Mathematics from Austin College in 2016. He then received his PhD in Biophysics from the University of Illinois at Urbana-Champaign in 2023 under the guidance of Dr. Emad Tajkhorshid.

AI for Science Speaker

Marieme Ngom is an Assistant Computer Scientist at the Argonne Leadership Computing Facility. Her research interests include probabilistic machine learning, high-performance computing, and dynamical systems modeling with applications in chemical engineering and material sciences. Ngom received her Ph.D. in mathematics from the University of Illinois at Chicago (UIC) in 2019 under the supervision of Prof. David Nicholls. Marieme holds an MSc in mathematics from the University of Paris-Saclay (formerly Paris XI), an MSc in computer science from the National Polytechnic Institute of Toulouse, and an MEng in computer science and applied mathematics from the École nationale supérieure d’électrotechnique, d’électronique, d’informatique, d’hydraulique et des télécommunications (ENSEEIHT) in Toulouse. She will be discussing LLM Evaluation.

Conveners: Archit Vasan (LCF), Marieme Ngom (LCF), Shilpika . (LCF)