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The Large Hadron Collider generates tens of thousands of exabytes of raw data annually, making real-time AI and machine learning essential for filtering millions of collision events per second to find rare, meaningful signals. Dr. Thea Aarrestad offers an inside look at how physicists and engineers push the limits of data, speed, and precision to make discoveries like the Higgs boson possible.
Thea Aarrestad is a fellow at the Institute for Particle Physics and Astrophysics at ETH Zürich. She holds a PhD in Particle Physics from the University of Zürich and has worked as a research fellow at CERN in Geneva before moving to ETH. Her research centers on how Machine Learning can be applied to particle physics problems, especially focusing on using real-time Machine Learning (ML) for discovering new physics phenonema. She has worked on tools for performing low-power, nanosecond ML inference on field-programmable gate arrays (FPGAs), as well as developing new methods for collecting and analyzing proton collision data at the CERN Large Hadron Collider using ML-based anomaly detection techniques.
