Machine learning on data is typically performed after it is gathered. But advances in real-time machine learning can analyze data on the fly, allowing scientists to quickly adjust experiments to capture phenomena of interest. That’s particularly appealing to researchers using the Large Hadron Collider at CERN, where a single on-chip system can absorb multiple terabytes of data each second. David Miller, associate professor of physics at UChicago, Nhan Tran, Wilson Fellow at Fermilab, and Andrew A. Chien, professor of computer science at UChicago, will collaborate on the design of new hardware that enables these advanced real-time processes. The resulting “system-on-a-chip” hardware design would help both high-energy physicists and researchers in other data-intensive fields monitor data quality and detect promising results without interrupting the flow of data.
Associate Professor of Physics, University of Chicago
Wilson Fellow, Fermilab
Professor of Computer Science, University of Chicago