We are pleased to announce a four-day event Fast Machine Learning for Science, which will be hosted by Purdue University from October 15-18, 2024. 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. For those attending in person, there will be a social reception during the evening of Tuesday, October 15, and a dinner on Thursday, 17th.
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 accelerate discovery greatly. 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 16th, 2024
Registration deadline: October 1st,2024
Organising Committee:
Mia Liu (Chair)
Maria Dadarlat (Co-chair)
Andy Jung
Norbert Neumeister
Wei Xie
Paul Duffel
Haitong Li
Guang Ling
Eugenio Culurciello
Yong Chen
Alexandra Boltasseva
Laimei Nie
Scientific Committee:
Thea Aarrestad (ETH Zurich)
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)
Jennifer Ngadiuba (Fermilab)
Maurizio Pierini (CERN)
Sioni Summers (CERN)
Alex Tapper (Imperial College)
Nhan Tran (Fermilab)
