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Our seminar series is currently on a break. Contact DSI-Seminars [at] llnl.gov (DSI-Seminars[at]llnl[dot]gov) with any questions.

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Data Scientist Spotlight

Leno da Silva

Leno da Silva

Reinforcement Learning Researcher

Leno da Silva is a reinforcement learning researcher in LLNL’s Computational Engineering Department where he intends to make an impact with his work. Leno works primarily on the Generative Unconstrained Intelligent Drug Engineering (GUIDE) project developing machine learning approaches for the rapid design of antibody therapeutics. Leno says he is grateful to be working on data science and artificial intelligence (AI) at such a pivotal time, noting, “It is exciting and challenging to keep up with the accelerated pace of research on AI and contribute with advances in applications of relevance to national defense.” Leno has contributed to several other projects including smart transportation, AI-powered power converter design, and AI-based sepsis treatments, and he now serves as coordinator for the DSI Seminar Series, which features innovators and thought leaders in academia, industry, and national labs. Leno also mentors students and sees the experience as a way to share knowledge and learn with a new generation of researchers. Before his time at LLNL, Leno completed his PhD at the University of São Paulo, Brazil, and worked as a postdoc at the Advanced Institute for AI. He has published several papers in the past year and co-organized the Lab’s AI Safety Workshop in April.

Recent Research

Redesigning Antibodies Against Viral Pandemics

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In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromised by viral evolution. The team’s research is published in the journal Nature and showcases a novel antibody design platform comprising experimental data, structural biology, bioinformatic modeling and molecular simulations—driven by a machine-learning algorithm. With funding from the Department of Defense’s Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense’s (JPEO-CBRND’s) Generative Unconstrained Intelligent Drug Engineering (GUIDE) program, the interagency team used the platform to computationally optimize an existing SARS-CoV-2 antibody to restore its effectiveness to emerging SARS-CoV-2 Omicron subvariants, while ensuring continued efficacy against the then-dominant Delta variant. Their computational approach has the potential to significantly accelerate the drug-development process and improve pandemic preparedness. Read more about GUIDE.

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