Jul 16 – 26, 2022
US/Pacific timezone

Physics Community Needs, Tools, and Resources for Machine Learning

Jul 18, 2022, 7:00 PM
2h 20m
211 South Ballroom (HUB)

211 South Ballroom



Elham E Khoda (University of Washington)


Machine learning (ML) is becoming an increasingly important component of
cutting-edge physics research, but its computational requirements present significant challenges. In this poster, we discuss the needs of the physics
community regarding ML across latency and throughput regimes, the tools and
resources that offer the possibility of addressing these needs, and how these can
be best utilized and accessed in the coming years.

In-person or Virtual? In-person

Primary authors

Abhijith Gandrakota (Fermi National Accelerator Laboratory) Dylan Rankin (Massachusetts Institute of Technology) Elham E Khoda (University of Washington) Burt Holzman (FNAL) Christian Herwig (FNAL) Deming Chen (University of Illinois at Urbana-Champaign) Erik Katsavounidis (MIT) Georgia Karagiorgi (Columbia University) Javier Duarte (University of California San Diego) Jennifer Ngadiuba (FNAL) Kevin Pedro (Fermilab) Mark Neubauer (University of Illinois at Urbana-Champaign) Miaoyuan Liu (Purdue University) Michael Coughlin (University of Minnesota) Nhan Tran (FNAL) Philip Harris (MIT) Scott Hauck (University of Washington, Seattle) Shih-Chieh Hsu (University of Washington) Tingjun Yang (Fermilab) William McCormack (MIT) Yongbin Feng (Fermilab)

Presentation materials