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 (GECCO), 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.