Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics

America/New_York
Description

FWAM

Flatiron-wide Algorithms and Mathematics (FWAM) is a 2 day internal conference with the goal of overviewing/introducing a range of numerical algorithms and tools that are essential to research done at Flatiron and beyond. We also aim to form research connections across (and within) the centers, and showcase some of the research which makes use of these methods. Topics have been chosen that are crucial to two or more centers.

An email with registration information was sent to all Flatiron members.

Recordings of the presentations can be found HERE.

  • Monday, October 19
    • 10:00 AM 11:00 AM
      Tutorial: Introduction to Probabilistic Programming for Bayesian Inference with Stan
      Convener: Bob Carpenter (CCM)
    • 11:00 AM 11:20 AM
      Break/Discussion 20m
    • 11:20 AM 11:50 AM
      Case Study: Advanced MCMC methods
      Convener: Erik Thiede (CCM)
    • 11:50 AM 12:20 PM
      Case Study: Hierarchical low rank compression and an application in many-body quantum physics
      Convener: Jason Kaye (CCM)
    • 12:20 PM 1:00 PM
      Lunch/Discussion 40m
    • 1:00 PM 2:00 PM
      Tutorial: Equivariant neural nets for physical systems
      Convener: Risi Kondor (CCM)
    • 2:00 PM 2:20 PM
      Break/Discussion 20m
    • 2:20 PM 2:50 PM
      Case Study: Recurrent VAE for anomaly detection in supernova time series
      Convener: Ashley Villar (Simons Society of Fellows)
    • 2:50 PM 3:20 PM
      Case Study: Differentiable programming for protein structure alignment
      Convener: Jamie Morton (CCB)
    • 3:20 PM 3:40 PM
      Break/Discussion 20m
    • 3:40 PM 4:40 PM
      Tutorial: A Mathematical Introduction to Variational Quantum Monte Carlo with Deep Neural Networks
      Convener: James Stokes (CCQ)
    • 4:40 PM 5:00 PM
      Break/Discussion 20m
  • Tuesday, October 20
    • 10:00 AM 11:00 AM
      Tutorial: Inverse problems, sparsity and neural network priors
      Convener: Marylou Gabrie (CCM)
    • 11:00 AM 11:20 AM
      Break/Discussion 20m
    • 11:20 AM 11:50 AM
      Case Study: Descartes versus bayes: Views of deep net theories
      Convener: Stephane Mallat (CCM)
    • 11:50 AM 12:20 PM
      Case Study: SARS-CoV-2 transmission in Marine recruits during quarantine and training
      Convener: Rachel Sealfon (CCB)
    • 12:20 PM 1:00 PM
      Lunch/Discussion 40m
    • 1:00 PM 2:00 PM
      Tutorial: Interpretable machine learning with symbolic regression and graph networks
      Convener: Miles Cranmer (Princeton)
    • 2:00 PM 2:20 PM
      Break/Discussion 20m
    • 2:20 PM 2:50 PM
      Case Study: Discovering symbolic models in physical systems using deep learning
      Convener: Shirley Ho (CCA)
    • 2:50 PM 3:20 PM
      Case Study: An automated framework for efficiently designing deep convolutional neural networks in genomics
      Convener: Zijun Zhang (CCB)
    • 3:20 PM 3:40 PM
      Break/Discussion 20m
    • 3:40 PM 4:40 PM
      Tutorial: Tensor networks and ITensor
      Convener: Miles Stoudenmire (CCQ)
    • 4:40 PM 5:00 PM
      Break/Discussion 20m