Jose Antonio Lara Benitez

Biography

Jose Antonio Lara Benitez is a Postdoctoral Researcher at ICSI and NESAP for Machine Learning Postdoctoral Fellow at NERSC, Lawrence Berkeley National Laboratory. An applied mathematician, he develops structured scientific machine learning methods for the stable simulation of fluid dynamics, nonlinear partial differential equations (PDEs), and multiscale systems.

Dr. Benitez received his Ph.D. in Computational and Applied Mathematics from Rice University and an M.Sc. in Mathematics, with honors, from the Universidad Nacional Autónoma de México (UNAM). His research centers on embedding mathematical structure, such as conservation laws and stability guarantees, into machine learning models for scientific simulation. By combining rigorous numerical analysis with modern deep learning, he aims to build methods that remain accurate and stable over long simulations of complex physical systems, closing the gap between the flexibility of data-driven models and the reliability demanded by real-world scientific computing.

Dr. Benitez’s technical interests include applied and computational mathematics, scientific machine learning, numerical methods for PDEs, and high-performance computing. Through his work, he is committed to advancing physically consistent machine learning that makes the simulation of complex systems faster, more stable, and more trustworthy for scientific discovery.

Areas of Expertise

• Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
• Deep Learning and Neural Networks
• AI for Climate Change, Energy, and Environmental Sustainability
• High-Performance Computing (HPC) and Supercomputing
• Statistical Modeling, Bayesian Methods, and Inference
• Mathematical Optimization, Operations Research, and Decision Science
• Theoretical Foundations of AI and Machine Learning
• Digital Twins for Simulation, Prediction, and Optimization

ICSI Activity