Big Data, Fast Decisions: Real-Time Machine Learning to Accelerate Scientific Discovery
ORAL · Invited
Abstract
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.
*JN is supported by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with theDepartment of Energy (DOE), Office of Science, Office of High Energy Physics. JN is also supported by the U.S. Department ofEnergy (DOE), Office of Science, Office of High Energy Physics “Designing efficient edge AI with physics phenomena” Project(DE-FOA-0002705). JN is also supported by the DOE Office of Science, Office of Advanced Scientific ComputingResearch under the “Real-time Data Reduction Codesign at the Extreme Edge for Science” Project (DE-FOA-0002501). Thiswork was supported in part by the AI2050 program at Schmidt Futures (Grant G-23-64934).
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Presenters
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Jennifer Ngadiuba
- Fermi National Accelerator Laboratory (Fermilab)