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

Meet your mentors

Program Director: Cindy Gonzales is the Data Science Team Lead for the Biosecurity and Data Science Applications Group in LLNL’s Global Security Computing Applications Division. She originally joined the Lab as an administrator, but after attending a machine learning seminar, she was inspired to embark on a data science career. She spent years completing her education while working full-time at LLNL and started as a data scientist at LLNL in 2019. Her research interests include using machine learning for object detection and tracking in unconventional types of imagery. She earned her B.S. in Statistics from Cal State East Bay and her M.S. in Data Science from Johns Hopkins University. Cindy is also involved in several initiatives that promote diversity and inclusion in STEM, is a long-distance runner, and is the proud mother of two young children.

Program Director: Brian Gallagher, a computer scientist, returns to direct the 2023 Data Science Challenge program. “My main goal for the Challenge 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.

Lead Mentor: Mikel Landajuela is a machine learning researcher in LLNL’s Computational Engineering Division. His research interests include scientific computing, machine learning, computational mechanics, and mathematical modeling. Honored with the SMAI-GAMNI (French Society of Industrial and Applied Mathematics) award in 2017 for the best thesis in numerical methods for the mechanical and engineering sciences, Mikel holds a PhD in Computational and Applied Mathematics from Université Pierre-et-Marie-Curie and is part of the LLNL team that claimed a top prize at a 2022 international symbolic regression competition.