Solving Complex Scientific Problems with Machine Learning & HPC (Advanced Computing Series)
Remote event
In this webinar, we address a real-world physics problem using machine learning (ML) and artificial intelligence (AI) models. This talk is aimed at students and early-career researchers looking to get started with AI modeling. We will cover the essentials of data curation, cleaning, and exploratory data analysis. Alongside reviewing core ML/AI concepts and best practices, we will introduce several models, highlighting their respective advantages and drawbacks. Finally, we will apply these models to experimental data, analyze the results, and demonstrate how to accelerate workflows using High-Performance Computing (HPC) systems.
Instructor
Javier Hernandez-Nicolau
Data Scientist, SDSC
Javier Hernandez-Nicolau is a Data Scientist in the Scientific applications group at SDSC. He obtained his PhD in Plasma Physics and Nuclear Fusion at the University of Carlos III of Madrid (Spain) in 2019 and then worked as a Project Scientist at the University of California-Irvine. He developed and optimized MHD and gyrokinetic fusion plasma codes for several HPC systems using Fortan, OpenMP and OpenACC. He joined SDSC in 2023 where he keeps collaborating with the Nuclear Fusion community developing ML/AI applications. He also offers user support to deploy a large variety of AI models on the Voyager machine which uses Intel Gaudi accelerators.