Our mission at the Data Science Institute (DSI) is to enable excellence in data science research and applications across the Laboratory's core missions.

Data science has become an essential discipline paving the path of LLNL's key program areas, and the Laboratory is home to some of the largest, most unique, and most interesting data and supercomputers in the world. The DSI acts as the central hub for all data science activity—in areas of artificial intelligence, big-data analytics, computer vision, machine learning, predictive modeling, statistical inference, uncertainty quantification, and more—at LLNL working to help lead, build, and strengthen the data science workforce, research, and outreach to advance the state-of-the-art of our nation's data science capabilities. Read more about the DSI.

Data Scientist Spotlight

Hyojin Kim

Hyojin Kim

Data Scientist and Machine Learning Researcher

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.


New Research in AI: Cancer Research Goes Exascale

An LLNL team will be among the first researchers to perform work on the world’s first exascale supercomputer—Oak Ridge National Laboratory’s Frontier—when they use the system to model cancer-causing protein mutations. Led by LLNL computer scientist Harsh Bhatia, the team was awarded limited access to Frontier under the DOE's Advanced Scientific Research Center Leadership Computing Challenge program. Over the next year, Bhatia and his team will use the cycles to advance their previous work, applying their Multiscale Machine-Learned Modeling Infrastructure (MuMMI) computing framework and artificial intelligence to model and predict how RAS and RAF proteins interact with each other and with lipids on a realistic cell membrane. Read more at LLNL News.

progression of the MuMMI model to predict how RAS and RAF proteins interact with each other
LLNL computational biologist Helgi Ingólfsson added that the team is excited to expand and demonstrate their MUMMI framework on Frontier. “Addressing the needs of exascale—scalability and throughput, effective use of heterogeneous resources and AI-driven simulations—are all challenges that will be useful even beyond our current work on cancer research and could translate to other important applications,” he said.

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five plots showing DJINN prediction across different experiment results of yield, bang time, BW, Tion, and RhoR with explanantory text of “TL models are more predictive of future ICF experiments than simulations alone”

Lab researchers win top award for ML-based approach to ICF experiments

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