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Data Scientist Spotlight

James Diffenderfer

James Diffenderfer

Machine Learning Postdoctoral Researcher

James Diffenderfer is a machine learning (ML) researcher in LLNL’s Center for Applied Scientific Computing. He currently contributes to two ML-related projects: ZOO, or zeroth-order optimization for scientific ML, and AsyncML, asynchronous circuit design for dynamic ML adaptation. Much of his data science career has focused on adapting ML to real-world settings, ensuring that models function when they’re compressed or exposed to changes in data. Diffenderfer received an extensive education in mathematics, culminating in a PhD in Applied Mathematics and an MS in Computer Science from the University of Florida in 2020. He began his work at LLNL as an intern during his graduate studies. Now, as a researcher, he’s co-authored two recently accepted papers (in NeurIPS and ICLR) and presented at the 2023 Monterey Data Conference. Diffenderfer attributes much of his growth and success as a researcher to the mentorship he’s received at LLNL. He now prioritizes giving back, and he’s been mentoring summer interns since 2021. “I feel a sense of responsibility and privilege to serve as a mentor to student interns, and I hope that they can learn and grow from the experience as I did,” he says.

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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