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

Frank Di Natale

Frank Di Natale

Computer Scientist

Frank Di Natale looks for ways to more easily and effectively harness compute power, especially when a real-world problem is at stake. He says, “Leaning on simulations to better understand our world requires making compute accessible and facilitating sound simulation software and tools.” A notable example is the research described in the SC19 Best Paper. Di Natale and researchers from several organizations developed the novel Multiscale Machine-Learned Modeling Infrastructure (MuMMI) that predictively models the dynamics of RAS protein interactions with lipids in cell membranes. RAS protein mutations are linked to more than 30% of all human cancers. MuMMI’s machine learning algorithm selects lipid “patches” for closer examination while reducing compute resources. As lead author of the winning paper, Di Natale is proud of the team’s accomplishment and excited for MuMMI’s next phase: atomistic-scale protein simulation. “It’s exciting to design a multi-component system that produces the computational techniques that explore science in new ways,” he explains. Di Natale, who came to the Lab in 2016 after a stint at Intel Corporation, has an M.S. in Computer Science from the University of Colorado at Boulder. He is also the PI for the open-source Maestro Workflow Conductor software.


New Research in AI

A team led by LLNL computer scientist Jay Thiagarajan has developed a new approach for improving the reliability of artificial intelligence and deep learning-based models used for critical applications, such as health care. Thiagarajan recently applied the method to study chest X-ray images of patients diagnosed with COVID-19, arising due to the novel SARS-Cov-2 coronavirus. Read more at LLNL News.

series of chest x-ray images
This series of images depicts the progression of a patient diagnosed with COVID-19, emulated using the team’s calibration-driven introspection technique.

 

Data Science Institute

 

We're tackling COVID-19 research. Access the data portal.

protein structure of SARS-CoV-2

 

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