About me

I lead AI and scientific-computing teams at Lawrence Livermore National Laboratory in the Computational Engineering Directorate. My work spans machine learning, reinforcement learning, and high-performance scientific computing. I earned my Ph.D. from Université Pierre et Marie Curie & Inria in Paris, and received the SMAI-GAMNI award 2017 from the French Society of Industrial and Applied Mathematics.

Expertise: machine learning · reinforcement learning · optimization · scientific computing · mathematical modeling

AI for Science & Industry : Developing data-driven and physics-based algorithms on GPU/HPC clusters. Currently building deep-learning pipelines for protein design and medical countermeasures.
Interpretable & Responsible AI : Crafting symbolic-regression methods and decision-tree controllers to ensure transparent, auditable ML workflows suitable for regulated environments.
High-Performance Scientific Computing : Designing scalable PDE solvers for fluid-structure interaction and cardiovascular mechanics, optimizing C++/Python code across on-prem and cloud HPC environments.

I welcome research collaborations and industry partnerships. ✉️ landajuelala1 at llnl dot gov · 🔗 Google Scholar

News

2025-03-28: Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape published in Science Advances.
2024-05-08: Computationally restoring the potency of a clinical antibody against Omicron published in Nature.
2023-01-09: POLITICO magazine highlights our DoD collaboration on modernizing chemical-biological defense for the US government. (read article)
2022-09-14: A Unified Framework for Deep Symbolic Regression accepted at NeurIPS 2022. (openreview)
2022-07-13: 1st Place, “Interpretable Symbolic Regression for Data Science Competition: Real-world track” at GECCO 2022. (details)
2021-06-21: Discovering Symbolic Policies with Deep Reinforcement Learning accepted at ICML 2021. (paper)