Scientific Machine Learning Workshop

Sponsored by the U.S. Department of Energy
Office of Advanced Scientific Computing Research
North Bethesda, MD
January 30 - February 1, 2018

Program Manager: Steven Lee (DOE)

This Workshop is Currently by Invitation Only

The U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research will host a Workshop on Scientific Machine Learning in North Bethesda, MD. The meeting will be Tuesday at 8:00 am and end on Thursday, around 3:00 pm. Writers will continue on Thursday until 5:00 pm. Breakfast, lunch, and snacks will be included.

Scientific machine learning recognizes that interest in machine learning-based approaches for science and engineering applications continues to soar. This growing interest is due to the development of efficient analysis approaches, the availability of massive amounts of data from scientific instruments and other sources, advances in high-performance computing, and the successes reported by industry, academia, and research communities. The workshop will define and lead to a report on the challenges and opportunities for applied mathematics research to increase the rigor, robustness, and reliability of machine learning for DOE mission requirements.

Organizing Committee

  • Mark Ainsworth (Brown University)
  • Frank Alexander (Brookhaven National Lab)
  • Nathan Baker (Pacific Northwest National Lab)
  • Timo Bremer (Lawrence Livermore National Lab)
  • Aric Hagberg (Los Alamos National Lab)
  • Yannis Kevrekidis (Princeton University)
  • Habib Najm (Sandia National Labs)
  • Manish Parashar (Rutgers University)
  • Abani Patra (SUNY Buffalo)
  • James Sethian (University of California Berkeley)
  • Chris Symons (Oak Ridge National Lab)
  • Stefan Wild (Argonne National Lab)
  • Karen Willcox (MIT)