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

Data Scientist Spotlight

Emilia Grzesiak

Emilia Grzesiak

Data Scientist

With Biomedical Engineering degrees from Duke University, Emilia Grzesiak contributes to LLNL’s COVID-19 research by comparing simulations to bioassays that measure the binding affinity between the virus’s variants and antibody candidates. She also builds analysis and visualization tools to identify antibody designs that could be useful drug candidates. “I’m excited to help with therapeutic design decision-making and speed up the drug-design process,” she says. Grzesiak joined LLNL’s Global Security Computing Applications Division in 2021 after interning with the Data Science Summer Institute (DSSI) the previous year. Now, as a first-time mentor, she states, “I’m figuring out when to let go of the reins and when to step in more. Establishing trust and open communication is important, as making those judgment calls becomes easier when you understand how your intern approaches problems and what kind of advice they respond best to.” Grzesiak recently shared her career journey and research highlights during a DSI-sponsored panel discussion and a seminar for the DSSI's class of 2022.


New Research in AI: Top Award at International Symbolic Regression Competition

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An LLNL team claimed a top prize at an inaugural international symbolic regression competition for an artificial intelligence (AI) framework they developed capable of explaining and interpreting real-life COVID-19 data. Hosted by the open source SRBench project at the 2022 Genetic and Evolutionary Computation Conference, the competition had two tracks—synthetic and real-world—and invited teams to submit their best symbolic regression algorithms. Organizers trained the models on datasets, assigned “trust ratings” and evaluated them for accuracy and simplicity. LLNL computer scientist Brenden Petersen and his team’s “Unified Deep Symbolic Regression” (uDSR) algorithm beat out 12 other teams on the real-world track—a task to build an interpretable predictive model for 14-day forecast counts of COVID-19 cases, hospitalizations and deaths in New York state. Read more at LLNL News.

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