Scholar
Mikel Landajuela
Google Scholar ID: Tl93fucAAAAJ
Lawrence Livermore National Laboratory
Citations & Impact
Citations
1,475
H-index
14
i10-index
15
Publications
20
Co-authors
46
list available
Resume (English only)
Academic Achievements
- 2025-09-23: Keynote speaker at AI for SCIENCE 2025, Ljubljana, Slovenia.
- 2025-03-28: Published 'Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape' in Science Advances.
- 2024-05-08: Published 'Computationally restoring the potency of a clinical antibody against Omicron' in Nature.
- 2023-01-09: POLITICO magazine highlights our DoD collaboration on modernizing chemical-biological defense for the US government.
- 2022-09-14: 'A Unified Framework for Deep Symbolic Regression' accepted at NeurIPS 2022.
- 2022-07-13: 1st Place, 'Interpretable Symbolic Regression for Data Science Competition: Real-world track' at GECCO 2022.
- 2021-06-21: 'Discovering Symbolic Policies with Deep Reinforcement Learning' accepted at ICML 2021.
Research Experience
- Lead AI and scientific-computing teams at Lawrence Livermore National Laboratory.
- Developing data-driven and physics-based algorithms on GPU/HPC clusters.
- Building deep-learning pipelines for protein design and medical countermeasures.
- Crafting symbolic-regression methods and decision-tree controllers to ensure transparent, auditable ML workflows suitable for regulated environments.
- Designing scalable PDE solvers for fluid-structure interaction and cardiovascular mechanics, optimizing C++/Python code across on-prem and cloud HPC environments.
Education
- Earned my Ph.D. from Université Pierre et Marie Curie & Inria in Paris.
Background
- 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.
Miscellany
- Welcoming research collaborations and industry partnerships.
