Tutorial Workshop on Machine Learning for Experimental Science

DataX - Accelerating Scientific Discovery at Princeton

May 13-14, 2022
Friend Center 101

Many scientific experiments generate large, multi-modal datasets, often in the form of time-series of different dimensionality. A particular challenge that scientists face in their workflows is comparing experiments to model and simulation, determining how close experiments match expected theory. The various analyses that scientists perform on these datasets can greatly be enhanced and accelerated by machine learning techniques, including recent deep learning and Bayesian inference techniques. The main objective of the workshop is to distill current machine learning techniques to a broad scientific audience at Princeton, and provide much needed research tools based on machine learning to advance their science. This should benefit mostly the Princeton research community but also the broader nearby research institutions. 

AGENDA  |   PARTICIPANTS  |  REGISTRATION

Workshop Organizers

Michael Churchill

Princeton Plasma Physics Laboratory

Hantao Ji

Department of Astrophysical Sciences
Princeton Plasma Physics Laboratory

William M. Tang

Department of Astrophysical Sciences
Center for Statistics and Machine Learning
Princeton Institute for Computational Science and Engineering
Princeton Plasma Physics Laboratory

Steering Community

Ryan Adams

Computer Science/CSML
(On Sabbatical for AY21-22)

Peter Melchior

Astrophysics/CSML

Mariangela Lisanti

Physics

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