Big Data, Fast Decisions: Real-Time AI to Accelerate Scientific Discovery

In the era of big data, the ability to make rapid, data-driven decisions is transforming scientific research across disciplines. At the forefront of this revolution is fast machine learning, which enables real-time insights at unprecedented scales. This talk will explore cutting-edge techniques in fast machine learning for high-energy physics, focusing on real-time data processing and decision-making. I will discuss the integration of AI at the edge, recent advancements in algorithms, and how these innovations are accelerating discovery in fundamental physics. From identifying rare events to optimizing complex systems, fast machine learning is pushing the boundaries of what is possible in scientific exploration.

Watch Video

Bio

Jennifer Ngadiuba has been a Wilson Fellow at Fermilab since 2021, advancing searches for new physics in collider data and developing AI-driven techniques to enhance collider experiments at the CERN LHC. She earned her PhD at the University of Zurich, then held research fellowships at CERN and Caltech, introducing AI methods for anomaly detection and fast FPGA inference in the hardware trigger systems of collider and neutrino experiments. At Fermilab, she keeps integrating these new methods while doing cutting-edge research that advance real-time system applications of AI. Her innovative work in AI has earned major recognition, including the U.S. Department of Energy's AI4HEP award, the 2023 AI2050 Fellowship from Schmidt Sciences, and the 2024 IUPAP Early Career Award.