Data Science Challenge Mentors


July 14–25, 2025

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Questions?

DataScienceChallenge [at] llnl.gov (DataScienceChallenge[at]llnl[dot]gov)

We’re building multidisciplinary teams to tackle real-world data science challenges. You could help advance world-class science this summer!

LLNL staff like to lead by example, and you'll work with some of our most talented and dedicated scientists.

Meet your mentors

Brian Gallagher

Student Internships Director: Brian Gallagher, a computer scientist, directs LLNL's Data Science Summer Internships program. “My main goal is to provide an environment where everyone can grow,” he says. When he’s not working with students, he leads the Data Science & Analytics Group at LLNL’s Center for Applied Scientific Computing. His primary research interest is machine learning for scientific applications. He contributes to several projects in the areas of nuclear threat-reduction, computer networks, and materials discovery and development. Brian joined LLNL in 2005 after earning an M.S. in Computer Science at the University of Massachusetts Amherst in 2004 and a B.S. in Computer Science from Carleton College in 1998.

Omar DeGuchy

DSC Lead: Omar DeGuchy works on the development and application of deep learning models for object detection across a variety of modalities. A former LLNL intern, Omar focuses his research on machine learning, deep learning, multimodal object detection, and adversarial machine learning. He holds a Ph.D. in Applied Mathematics from UC Merced and an M.S. in Mathematics from Cal Poly Pomona.

 

Mary Silva

Student Outreach Lead: Mary Silva works in LLNL’s Global Security Computing Applications Division on the GUIDE program and the multi-institutional SPOKE project. Mary is a former LLNL intern and now serves as ambassador to the global Women in Data Science (WiDS) organization. Her research interests include machine learning and AI for bioinformatics, protein structure modeling, and protein sequence modeling. Mary has an M.S. in Statistics and Applied Math from UC Santa Cruz and a B.S. in Applied Math from San Francisco State University.

Kerianne Pruett

Challenge Problem Lead: Kerianne Pruett works on various projects spanning LLNL’s mission areas—from astronomy and space domain awareness to image processing and conflict analysis. Her research expertise is in data analysis, data visualization, image classification, HPC data pipelining, and design of experiments. Kerianne started at LLNL as an intern after completing a B.S. in Astrophysics at UC Davis. Since joining the Lab, she has completed a Data Analytics Master’s certificate through the Air Force Institute of Technology, and is finishing an M.S. in Space Systems Operations at Naval Postgraduate School.