Highlights and opportunities in AI at Fermilab (Computational Science Seminar Series)

Speaker: Nhan Tran (FNAL)

Tuesday, November 12, 2019 from 10:30 to 11:30 (US/Central)

  • Attend in person at University of Chicago, John Crerar Library, Kathleen A Zar Room (first floor)
  • Watch simulcast at Fermilab, Racetrack (WH7X)
  • Watch remotely with Zoom (link to be provided soon – See https://indico.fnal.gov/event/22307/)

 

Abstract: In the first of the joint seminar series to build connections between the University of Chicago, Argonne, and Fermilab, we will highlight current activities in Artificial Intelligence (AI) at Fermilab. Machine learning techniques have long been at the core of particle physics. We will present recent scientific results deploying modern deep learning which enable the particle physics mission of Fermilab from neutrinos to cosmology to the energy frontier. We will also discuss the exciting opportunities at the intersection of particle physics and AI that can push the boundaries of new algorithms and hardware to power the next generation of experiments and scientific discovery.

Speaker Bio: Nhan Tran is currently a Wilson Fellow at Fermilab. Tran’s research focus is on using accelerator-based experiments, such as CMS at the LHC, to search for new phenomena. His current activities center on the Higgs boson and dark sectors experiments. He is developing technology at the intersection of electronics, computing, and artificial intelligence to amplify experimental capabilities. He was a postdoctoral associate at Fermilab and prior to that he received his PhD from Johns Hopkins University in 2011 and his bachelor’s degree from Princeton University in 2005. Tran is a recipient of the URA Tollestrup Award, the APS Henry Primakoff Award, and the DOE Early Career Award.

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