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

Nisha Mulakken

Nisha Mulakken

Biostatistician

Nisha Mulakken’s research lies at the confluence of biology, computer science, and statistics. Her work in LLNL’s Bioinformatics Group includes enhancing the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2, as well as analyzing mutations in SARS-CoV-2 proteins to support future discovery of therapeutic compounds. In another project, she uses machine learning to trace unethical use of CRISPR technology to the source lab. Mulakken was recently named the new co-director of the Data Science Summer Institute and looks forward to working with the class of 2021. She says, “I hope the students will experience the Lab’s collaborative culture, learn about academic topics and practical applications they may not have been exposed to yet, and genuinely enjoy getting to know each other and their mentors.” A four-time LLNL summer intern and longtime employee, Mulakken holds degrees in genetics and biostatistics.


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.

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