Oct 3 – 6, 2022
Southern Methodist University
America/Chicago timezone

We are pleased to announce a four-day event "Fast Machine Learning for Science”, which will be hosted by Southern Methodist University from October 3-6, 2022. The first three days will be workshop-style with invited and contributed talks.  The last day will be dedicated to technical demonstrations and satellite meetings.  The event will be hybrid with an in-person, on-site venue and the possibility to join virtually.  

As advances in experimental methods create growing datasets and higher resolution and more complex measurements, machine learning (ML) is rapidly becoming the major tool to analyze complex datasets over many different disciplines.  Following the rapid rise of ML through deep learning algorithms, the investigation of processing technologies and strategies to accelerate deep learning and inference is well underway.  We envision this will enable a revolution in experimental design and data processing as a part of the scientific method to greatly accelerate discovery. This workshop is aimed at current and emerging methods and scientific applications for deep learning and inference acceleration, including novel methods of efficient ML algorithm design, ultrafast on-detector inference and real-time systems, acceleration as-a-service, hardware platforms, coprocessor technologies, distributed learning, and hyper-parameter optimization.

Abstract submission deadline: September 7, 2022  September 9, 2022
Registration deadline: September 21, 2022

Organizing Committee:
Mohammed Aboelela (Southern Methodist University)
Susan Bataju (Southern Methodist University)
Thomas Coan (Southern Methodist University)
Allison McCarn Deiana (Southern Methodist University)
Jasmine Jennings (Southern Methodist University)
Fred Olness (Southern Methodist University)
Santosh Parajuli (Southern Methodist University)
Reagan Thornberry (Southern Methodist University)

Scientific Committee:
Allison McCarn Deiana (Southern Methodist University)
Javier Duarte (UCSD)
Phil Harris (MIT)
Burt Holzman (Fermilab)
Scott Hauck (U. Washington)
Shih-Chieh Hsu (U. Washington)
Sergo Jindariani (Fermilab)
Mia Liu (Purdue University)
Mark Neubauer (U. Illinois Urbana-Champaign)
Maurizio Pierini (CERN)
Nhan Tran (Fermilab)

The call for abstracts is open
You can submit an abstract for reviewing.