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Data Scientist Spotlight

Aneesha Devulapally

Aneesha Devulapally

Data Scientist

Aneesha Devulapally is a data scientist in the Global Security Computing Applications Division. She is particularly interested in the interdisciplinary field of bioinformatics and applications of machine learning (ML) in systems biology. As a part of LLNL’s GUIDE program, she develops ML frameworks and pipelines and performs data analysis on antibody–antigen complexes. “Data science brings subject matter experts from various domains together to collaborate in solving complex problems, especially at Livermore where we’re working towards solving problems for the betterment of humankind,” Devulapally says. She joined LLNL after earning her master’s in computer science with a specialization in data science from the University of Texas at Dallas after earning undergraduate and graduate degrees at IIIT Bangalore in India. Devulapally recently served on the organizing committee for Livermore’s 2024 Women in Data Science event. “The enthusiasm of the participants and their interest in learning data science concepts made the experience incredibly rewarding,” she 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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