Pursuing answers to fundamental questions about nature requires searches for the ultra-rare and very subtle and the inspection of nature at extremely fine spatial and temporal scales. Cutting-edge experiments are often confronted with massive amounts of very rich data on which Artificial Intelligence (AI) techniques provide powerful insights. To accelerate scientific discovery, enabling powerful AI algorithms across the data processing continuum, as close to sensor front-ends as possible, is becoming increasingly valuable. To deploy AI in these challenging scientific environments, we require robust and efficient learning and usable and accessible tool flows for optimized training and implementation across a broad range of scientific domains. This talk will introduce real-time AI applications for science, modern techniques for co-designing them into our experiments, and discuss how these efforts can be connected to the Genesis Mission.

Nhan Tran is currently a Scientist at Fermilab. He completed his undergraduate degree in physics from Princeton University in 2005 and his Ph.D. from Johns Hopkins University in 2011. He is the lead for the AI Program at Fermilab. His research is focused on accelerator-based experiments to search for new dark matter and Higgs phenomena, such as at the Compact Muon Solenoid (CMS) at the Large Hadron Collider (LHC), and the interface between AI, electronics, and instruments to advance science. Tran is a recipient of the URA Tollestrup Award, the APS Henry Primakoff Award, and the Presidential Early Career Award for Science and Engineering (PECASE).