Intro to AI-driven Science on Supercomputers: A Student Training Series

Oct 1, 2024, 3:00 PM → Nov 12, 2024, 4:30 PM America/Chicago

Zoom (Virtual Training Series)

The ability for artificial intelligence (AI) to successfully learn from large datasets has transformed science and engineering as we know it. AI can accelerate scientific discovery and innovation, but often requires more computing power than is available to most researchers. The DOE provides supercomputers to solve the nation’s biggest scientific challenges and this series aims to introduce a new generation of AI practitioners to these powerful resources.

Building on the ALCF's robust training program in the areas of AI and supercomputing, we are hosting a series of hands-on courses that will teach attendees to use leading-edge supercomputers to develop and apply AI solutions for the world's most challenging problems. This year, we will focus on understanding the fundamentals of large-language models (LLMs) and their scientific applications. 

Pre-requisites

This training series is aimed at undergraduate and graduate students enrolled at U.S. universities and community colleges. Attendees are expected to have basic experience with Python. No supercomputing or AI knowledge is required.

Workshop Series Format

Each session will have both lecture and hands-on components, along with a talk from an Argonne scientist about the work they do using AI for their science.

Each session occurs on Tuesdays from 3:00-4:30 p.m. CT. Session recordings will be made available shortly after each event. Series materials can be found on the series Github. 

Attendees who complete all in-class and post-class exercises by the end of the series will receive a certificate of completion and a digital badge.

Recordings for each session will be posted weekly on the session-specific pages below.

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 1 – November 12, 2024.

Tue, October 1

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)

AI Revolution for Weather and Climate.pdf

Session 1 Materials

Session 1 Recording

Tue, October 8

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)

Advancing materials characterization through physics-guided machine learning.pdf

Session 2 Materials

Session 2 Recording

Tue, October 15

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)

AI-driven Cosmology.pdf

Session 3 Materials

Session 3 Recording

Tue, October 22

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)

AI@Edge_ALCF_Oct22_2024.pdf

Session 4 Materials

Session 4 Recording

Tue, October 29

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)

LLM_evaluation_102024.pptx

Session 5 Materials

Session 5 Recording

Tue, November 5

Session 6: Parallel Training Methods for AI

Intro to AI Series: Session 6
We present modern parallelism techniques and discuss how they can be used to train and distribute large models across many GPUs.

Lecturer
Sam Foreman is a Computational Scientist with a background in high energy physics, currently working as a postdoc in the ALCF. He is generally interested in the application of machine learning to computational problems in physics, particularly within the context of high-performance computing. Sam's current research focuses on using deep generative modeling to help build better sampling algorithms for simulations in lattice gauge theory.

AI for Science Speaker
Arvind Ramanathan is a computational biologist in the Data Science and Learning Division at Argonne National Laboratory and a senior scientist at the University of Chicago Consortium for Advanced Science and Engineering (CASE). His research interests are at the intersection of data science, high performance computing and biological/biomedical sciences. He will be speaking about Autononous Discovery for Biological Systems Design.

Conveners: Arvind Ramanathan (DSL), Sam Foreman (LCF)

Integrating generative AI with automation and simulations for biological systems design.pdf

Session 5 Materials

Session 6 Recording

Tue, November 12

Session 7: AI Accelerators: AI Accelerators

Intro to AI Series: Session 7
Trainees will learn about the current advances in AI hardware and the ALCF AI Testbed that is being integrated with existing and upcoming supercomputers at the facility to accelerate science insights.

Lecturer
Siddhisanket (Sid) Raskar is an Assistant Computer Scientist in the AI/ML group at Argonne National Laboratory. Sid works on exploring AI accelerators' performance and efficiency for scientific machine learning applications. Sid's research is at the intersection of computer architecture, high-performance computing, and machine learning.

Sid obtained my Ph.D. under the guidance of Guang R. Gao on the topic of "Dataflow Software Pipelining" at the University of Delaware. Sid also has a Master's in Computer Science from the University of Delaware and a Bachelor's in Computer Engineering from the University of Pune.

Convener: Sid Raskar (LCF)

Session 7 Materials

Session 7 Recording