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

Harsh Bhatia

Harsh Bhatia

Computer Scientist

Harsh Bhatia’s research in scientific visualization is all about seeing the unseen. “In this field, we can develop new techniques that distill extremely complex data into comprehensible visual information,” he states. His wide range of projects include applying topological techniques to understand the behavior of lithium ions, generating topological representations of aerodynamics data, and analyzing and visualizing HPC performance data. Notably, Bhatia and his collaborators won the SC19 Best Paper Award for their work on the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), which predictively models protein interactions that can lead to cancer. He notes, “MuMMI offers a new paradigm that is arbitrarily scalable and promises to solve the problems no other technology can.” Bhatia was a Lawrence Graduate Scholar and an LLNL postdoctoral researcher before joining the Lab’s Center for Applied Scientific Computing full time in 2017. He holds a PhD from the University of Utah’s Scientific Computing and Imaging Institute.

New Research in AI

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This scenario has led to wide-spread adoption of AI-powered tools,in pursuit of improving accuracy and efficiency of this process. Recently published research in Nature Scientific Reports introduces DDxNet, a multi-specialty diagnostic model for clinical time-series. The team found DDxNet to be highly effective across different modalities, diagnosis tasks, and data fidelity. The DDxNet model (GitHub repo) utilizes adaptively dilated causal convolutions coupled with dense connections for effective feature learning. The paper is co-authored by LLNL data scientist Jay Thiagarajan.

Five EEG abnormality graphs labeled as seizure, tumor present, healthy, eyes open, and eyes closed
Example EEG abnormality detection data from the different benchmark diagnosis problems considered in the paper. Note that only 1 of the 22 EEG channels is shown.


Data Science Institute


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