WELCOME TO THE SciFM SUMMER SCHOOL


MICDE recently organized the SciFM conference and had an incredible reception for the event. Based on the feedback from that meeting and further discussions with National Lab and academic partners, we are excited to organize the SciFM Summer School from July 8 to July 26. We wish for this to be a recurring event hosted at different locations across the country. The first instance is going to be run in parallel at the University of Michigan (UM) and at Argonne National Laboratory (ANL). The focus will be on developing technical plans for specific instances for SciFMs; a hackathon to jumpstart the construction of SciFMs; as an incubator for ideas; a training ground for researchers and students; and to further a vision for a national SciFM ecosystem.

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Core Organizers

Venkat Viswanathan
Associate Professor of Aerospace & Mechanical Engineering
University of Michigan
Karthik Duraisamy
Karthik Duraisamy
Director of MICDE
University of Michigan
Arvind Ramanathan
Computational Science Leader 1
Argonne National Laboratory

Hackathon Organizers

Anima Anandkumar
Bren Professor of Computing and Mathematical Sciences
California Institute of Technology
Michael Mahoney
Group Lead Machine Learning and Analytics
Lawrence Berkeley National Laboratory
  • Date
    Agenda
  • July 8
    Overview of workshop goals and activities, Team forming
  • July 9
    Discussion of project plans for 3 weeks
  • July 10
    Tutorial 1
  • July 11
    Planning next steps for National SciFM ecosystem / SciFM collective
  • July 11 - 25
    Project work & Hackathon
  • July 26
    Final presentations and plans for continued collaboration

Participants will explore innovative applications of Foundation Model in four application domains:

  1. Materials -

    Foundation Models for material science have been used for a range of material property prediction, retrosysnthesis and structure search tasks.

  2. Biology -

    In Biology, Foundation Models have enable numerous breakthroughs including protein structure prediction and the classfication of emergent variants of viruses.

  3. Computational Science -

    A Computational Science Foundation Model capable of designing and orchestrating computational research campaigns will enable scientists to focus on domain science through the creative specification of hypotheses and greatly accelerate scientific discovery.

  4. Partial Differential Equations -

    Foundation Models may be the route to enabling data efficient, general purpose PDE solvers.