معرفی
Dr. Adam Aurisano is an experimental particle physicist specializing in neutrino physics and machine learning applications. He is affiliated with the University of Cincinnati's Department of Physics, focusing on long-baseline neutrino oscillations, sterile neutrino searches, and tau neutrino appearance studies. His research leverages deep learning and graph neural networks, particularly for DUNE experiment reconstruction.
- Education: A.B./S.B. in Physics/Mathematics (University of Chicago, 2004), M.S./Ph.D. in Physics (Texas A&M, 2007/2012)
Research interests include testing LSND/MiniBooNE anomalies via sterile neutrinos, and developing AI-driven tools for neutrino event analysis. He leads projects like Exa.TrkX for HEP tracking. Notable grants include DOE-funded studies on neutrino oscillations and NSF MRI cluster acquisitions. Collaborates with NOvA, DUNE, and MINOS/MINOS+ experiments.
His work bridges particle physics and machine learning, with recent focus on hierarchical graph networks for DUNE's tau neutrino reconstruction.
