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

Meet your mentors

Hyojin Kim is a computer scientist and machine learning researcher at LLNL’s Center for Applied Scientific Computing. His research interests span broad areas of machine learning and computer vision, particularly related to the applications for highly ill-posed computed tomography, AI-driven drug discovery, scalable and distributed deep learning, multi-modal image registration and fusion, and image analysis for automatic threat recognition. He also has hands-on experience applying GPU computing to challenging problems in these areas. Hyojin joined LLNL in 2013 after earning his Ph.D. in Computer Science from UC Davis in 2012.

Ryan Dana is a data scientist working in the Global Security Computing Applications Division and the astrophysical analytics group. Ryan was a student intern with LLNL’s Data Science Summer Institute in 2019 and graduated that year with a B.A. in Physics, Astrophysics, and Data Science from UC Berkeley. He joined the Lab as a full-time employee in early 2020 and mentored students in the 2021 Data Science Challenge program. Ryan’s research interests include using machine learning techniques to approach astrophysical questions.

Cindy Gonzales originally joined LLNL as an administrator, onboarding and offboarding summer students during their internships. After attending a machine learning seminar, she was inspired to embark on a data science career and now works as a data scientist in the Global Security Computing Applications Division. Her research interests include using machine learning to detect objects in unconventional types of imagery. She earned her B.S. in Statistics from Cal State East Bay and is planning to finish her M.S. in Data Science from Johns Hopkins University in Summer 2022. Cindy is also involved in several initiatives that promote diversity and inclusion in STEM.

Computer scientist Brian Gallagher returns to direct the 2022 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.