We are pleased to announce a four-day event "Fast Machine Learning for Science”, which will be hosted virtually by Southern Methodist University from November 30 to December 3. The first three days (Nov 30 - Dec 2) will be workshop-style with invited and contributed talks. The last day will be dedicated to technical demonstrations and coding tutorials.
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: October 30, 2020 3:59PM CDT
Organizing Committee:
Allison Deiana (Southern Methodist University)
Rohin Narayan (Southern Methodist University)
Thomas Coan (Southern Methodist University)
Elizabeth Fielding (Southern Methodist University)
Scientific Committee:
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)
Allison McCarn Deiana (Southern Methodist University)
Mark Neubauer (U. Illinois Urbana-Champaign)
Maurizio Pierini (CERN)
Nhan Tran (Fermilab)
