Loïc Pottier is a computer scientist at the Center for Applied Scientific Computing. Pottier earned his Ph.D. in Computer Science from the École Normale Supérieure (ENS) de Lyon, France and received an M.S. in Computer Science from the University of Versailles, France. He joined LLNL as a research scientist in 2022 after working at the University of Southern California as a postdoctoral researcher and a research scientist for three years.

His research interests include scheduling techniques for parallel systems, performance models and scientific workflows management. A large part of Pottier's research is focused on performance modeling for parallel platforms and I/O optimizations for scientific workflows. During his Ph.D, he developed novel analytical models to evaluate the performance of concurrent scheduling approaches on many-core architectures.

Pottier serves on various program committees and journals such as SC (workshops, ACM posters and reproducibility committees), IEEE IPDPS and Elsevier JPDC. He is also the organizer and co-chair of the First International Symposium on Artificial Intelligence and Extreme-Scale Workflows (AIExScale) held in conjunction with SC.

At LLNL, Pottier is involved in several projects involving large-scale workflows. Pottier is the workflow lead for the Multiscale Machine-Learned Modeling Infrastructure (MuMMI) workflow and he is one of the core developers of the Autonomous MultiScale (AMS) project.

Ph.D. Computer Science, ENS Lyon, Lyon, France

M.S. Computer Science, University of Versailles, Versailles, France

Google Scholar: https://scholar.google.com/citations?user=hL9eZlQAAAAJ&hl=en

• LLNL SPOT Award, 2025
• NSF CRII Award, 2021

GitHub: https://github.com/lpottier

Personal website: https://lpottier.github.io/

This personal website is not sanctioned or supported by DOE, UC, LLNL, or any other organization.