Maria K. Cameron

University of Maryland, Department of Mathematics


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RIT: ML for rare events

Organizer: Maria Cameron

Meetings: Friday, 2PM

Room: MTH1310

Synopsis

In recent years, machine learning techniques penetrated a tremendous variety of scientific fields. In particular, they gave rise to data-driven methods for the study of rare event in complex physical systems such as conformational changes in biomolecules, rearrangements of clusters of interacting particles, etc. These methods truly opened new horizons by enabling us to address problems that used to be intractable due to the curse of dimensionality. They are divided into two families: diffusion map-based and neural network-based. In this RIT we will explore methods for the study of rare events based on machine learning.

Program for Fall 2021

Each meeting, one of the participants will give a talk on a paper relevant to the subject of the RIT or on his/her research if it is related to ML and rare events. A list of possible papers to present is given in the overview slides. The participants are welcome to look for other papers on the subject as well.

Registration

There is an option for students to register for this RIT for one credit: AMSC689 section 0802. In order to register, students need to contact Jessica Saddler (jsadler at umd dot edu), the AMSC program coordinator, and provide their UID. Students who has registered are expected to give a talk.

Schedule for Fall 2021:

  1. Friday September 10, 2 PM.

    Maria Cameron, An overview. Slides
  2. Friday September 24, 2 PM.

    Luke Evans, Diffusion maps applied to molecular dynamics.
  3. Friday October 8, 2 PM.

    Margot Yuan, Quantifying rare events with the aid of neural networks.
  4. Friday October 15, 2 PM.

    Manyuan Tao, Analysis of activation functions for neural networks (based on A. Townsend's papers)
  5. Friday October 22, 2 PM.

    Christopher Moakler, Self-Assembly of Hydrocarbons
  6. Friday November 5, 2 PM.

    Maria Cameron, Mean-field analysis and scaling limits for neural networks (based on works by J. Sirignano and K. Spiliopoulos)
  7. Friday November 19, 2 PM.

    Ryan Synk, Density Functional Theory and ML
  8. Friday December 3, 2 PM.

    Shashank Sule, SpectralNet.

Previous meetings: Summer 2021