Projects and Mentors

blue spiral shapes twisted together to form a SARS-COV-2 protein

New technological advances and the cheapening of data acquisition have vastly expanded what is possible in bioinformatics. Things like predicting protein folding and interactions, which I previously believed impossible, are not anymore. My experience at LLNL has changed what I think is possible.

—Jonathan Anzules, DSSI class of 2022

As a DSSI intern, you’ll work on real projects with real data that represent the breadth and depth of data science research at LLNL. Our students tackle Challenge Problems that leverage large and varied datasets used in or generated from actual LLNL projects such as building networks from interaction data, large-scale data mining for predictive medicine, drug discovery using HPC simulations, video data summarization and classification, energy efficiency analysis using HPC, classification and forward modeling of hyper-spectral data, and much more. This curriculum helps students build technical experience and teamwork.

Our interns are also paired with mentors—experts across many data science fields at the Lab—whose projects align with students’ skills and interests. Check out recent Challenge Problems and mentors below.

2023 Challenge Problem

2022 Challenge Problem

2021 Challenge Problems

Mentor Spotlight

Hyojin Kim

Hyojin Kim

Hyojin Kim is a data scientist and machine learning researcher at LLNL’s Center for Applied Scientific Computing. His research interests in machine learning and computer vision are recently related to applications for computed tomography, AI-driven drug discovery, scalable and distributed deep learning, and multimodal image analysis. He also has hands-on experience applying GPU computing to challenging problems in these areas. Balancing research and development, as well as learning domain knowledge, are crucial because, Kim says, “I often see data scientists trying to apply a new technique to a particular domain application where it may not be suitable.” This summer, Kim mentored students from two University of California campuses in DSI’s Data Science Challenge to accelerate drug discovery for COVID-19. During the intensive two-week program, he states, “Many of the students I met were enthusiastic, and some of them came up with brilliant ideas that I never thought about before. Students majoring in fields other than computer science are quite knowledgeable in data science, and I actually feel the growing popularity of data science in recent years.” Kim joined LLNL in 2013 after earning his Ph.D. in Computer Science from UC Davis in 2012.

Ready for the next step?

Visit our applications page to read FAQ about the program, apply to the job posting, or contact us for more information. Join the LLNL Scholars Google Group to chat with mentors and other students.