Tutorial Workshop on Machine Learning for Experimental Science

 

DataX - Accelerating Scientific Discovery at Princeton

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
    (On Sabbatical for AY21-22)

  • Mariangela Lisanti

    Physics

  • Peter Melchior

    Astrophysics/CSML

Upcoming Events

October 1, 2021 | Best Practices in Python Packaging

Presented by

  • Brian Arnold - DataX Data Scientist
  • Vineet Bansal - Senior Research Scientist at Princeton Research Computing

Read more and register here.

April 2022 | Tutorial Workshop on Machine Learning for Experimental Science

Presented by

  • Michael Churchill - Staff Research Physicist, PPPL
  • Hantao Ji - Professor, Astrophysical Sciences
  • William Tang - Principal Research Physicist, PPPL

Click here for more information.

June 2-3, 2022 | DataX Workshop: Synthetic Control Methods

Presented by

  • Matias Cattaneo -  Professor, Operations Research and Financial Engineering

Click here for more information.

Sponsors

We gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation and our Princeton University partners:

CSMLDataX - Accelerating Scientific Discovery at Princeton