About me

I am a principal investigator and team lead at the Lawrence Livermore National Laboratory in the Computational Engineering Directorate. I work in machine learning and scientific computing. I earned my Ph.D. from Université Pierre et Marie Curie and Inria in Paris, France. I received the SMAI-GAMNI award 2017 by the French Society of Industrial and Applied Mathematics. My areas of expertise include machine and reinforcement learning, scientific computing, computational mechanics, and mathematical modeling.

I work on projects focused on:

  • AI for Science : I am developing data-driven and physics-based algorithms for solving large-scale problems in science and engineering, with a focus on health sciences. My approach combines high performance computing and novel machine and reinforcement learning algorithms. I am currently working on developing deep learning algorithms for protein design for medical countermeasures.
  • Interpretability in AI : The field of interpretability in AI focuses on developing machine learning algorithms that can be understood and explained by humans. This is important because it allows us to trust and use the predictions and decisions made by these algorithms in real-world applications. I am working on interpretable AI for prediction (e.g., discovering mathematical equations for from data) and for control (e.g., decision trees as controllers).
  • Scientific Computing : I am developing numerical methods for solving large-scale problems in computational mechanics, with an emphasis on cardiovascular mechanics. This involves developing high performance computing algorithms for solving partial differential equations, such as those describing fluid-structure interaction problems, fluid dynamics problems and other problems in computational mechanics.

I am open to research discussions and collaborations. Please feel free to contact me at landajuelala1 at llnl dot gov. More details about my research can be found on my Google Scholar profile.

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