Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics

Name: Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics
Start: 2020-10-19T10:00:00-04:00
End: 2020-10-20T17:00:00-04:00
Location: No location set

Oct 19, 2020, 10:00 AM → Oct 20, 2020, 5:00 PM 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
- Mon, Oct 19
- Tue, Oct 20
- Tutorial: Introduction to Probabilistic Programming for Bayesian Inference with Stan
  
  Convener: Bob Carpenter (CCM)
- 11:00 AM
  
  Break/Discussion
- Case Study: Advanced MCMC methods
  
  Convener: Erik Thiede (CCM)
- Case Study: Hierarchical low rank compression and an application in many-body quantum physics
  
  Convener: Jason Kaye (CCM)
- 12:20 PM
  
  Lunch/Discussion
- Tutorial: Equivariant neural nets for physical systems
  
  Convener: Risi Kondor (CCM)
- 2:00 PM
  
  Break/Discussion
- Case Study: Recurrent VAE for anomaly detection in supernova time series
  
  Convener: Ashley Villar (Simons Society of Fellows)
- Case Study: Differentiable programming for protein structure alignment
  
  Convener: Jamie Morton (CCB)
- 3:20 PM
  
  Break/Discussion
- Tutorial: A Mathematical Introduction to Variational Quantum Monte Carlo with Deep Neural Networks
  
  Convener: James Stokes (CCQ)
- 4:40 PM
  
  Break/Discussion
Tuesday, October 20
- Mon, Oct 19
- Tue, Oct 20
- Tutorial: Inverse problems, sparsity and neural network priors
  
  Convener: Marylou Gabrie (CCM)
- 11:00 AM
  
  Break/Discussion
- Case Study: Descartes versus bayes: Views of deep net theories
  
  Convener: Stephane Mallat (CCM)
- Case Study: SARS-CoV-2 transmission in Marine recruits during quarantine and training
  
  Convener: Rachel Sealfon (CCB)
- 12:20 PM
  
  Lunch/Discussion
- Tutorial: Interpretable machine learning with symbolic regression and graph networks
  
  Convener: Miles Cranmer (Princeton)
- 2:00 PM
  
  Break/Discussion
- Case Study: Discovering symbolic models in physical systems using deep learning
  
  Convener: Shirley Ho (CCA)
- Case Study: An automated framework for efficiently designing deep convolutional neural networks in genomics
  
  Convener: Zijun Zhang (CCB)
- 3:20 PM
  
  Break/Discussion
- Tutorial: Tensor networks and ITensor
  
  Convener: Miles Stoudenmire (CCQ)
- 4:40 PM
  
  Break/Discussion

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Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics