Tutorial Workshop on Machine Learning for Experimental Science

Princeton University is actively monitoring the situation around coronavirus (COVID-19) and the evolving guidance from government and health authorities. The latest guidance for Princeton members and visitors is available on the University’s Emergency Management website

Due to ongoing concerns and public safety and health restrictions associated with COVID-19, this workshop has been postponed.  Additional details will be provided at a later date.

Tutorial Workshop on Machine Learning for Experimental Science

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. 

 

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...

Steering Community

  • Ryan Adams

    Computer Science/CSML

  • Mariangela Lisanti

    Physics

  • Peter Melchior

    Astrophysics/CSML