Data Science Seminar / COMPASS

When:  Mondays every week
Time:   11:15pm - 12:15pm
Where: CSML Main Seminar Room
Organizers: Peter Melchior (melchior[at]astro.princeton.edu); Christina Kreisch (ckreisch[at]astro.princeton.edu); Lachlan Lancaster (lachlanl[at]princeton.edu)

Every week we discuss data science methods and applications from papers, reviews, software releases, etc. We also have demos for useful/fancy methods by locals and visitors through the COMPASS program.

The setting is informal. Material should be presented directly from the source or on the white board, demos should be hands-on. We collect links to documents, source code, tutorials, etc. for later perusal in this github repo.

If you would like to present at the data science seminar, please contact the organizers.

Date Location Speaker   Title
09/14/2018 140 Peter Melchior Introduction to PyTorch
09/24/2018 033 Elinor Medezinski Galaxy detection and identification using deep learning and data augmentation
10/01/2018 033 Peter Melchior The Unreasonable Effectiveness of Recurrent Neural Networks
10/8/2018 033 Peter Melchior Understanding LSTM Networks
10/15/2018 033 Lachlan Lancaster Deep neural networks to enable real-time multimessenger astrophysics
10/22/2018 Vislab Patrick Crumley Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
11/05/2018 Vislab Dan Foreman-Mackey (CCA) Practical Hamiltonian Monte Carlo in Python for Astronomers
11/12/2018 Vislab Boris Leistedt (NYU) Forward, causal modeling of galaxy photometry: Hierarchical causal models ↔ machine learning
11/19/2018 Vislab John Wu (Rutgers) Using deep convolutional neural networks to predict galaxy metallicity from three-color images
12/03/2018 033 Remy Joseph Sparse regularisation for strong gravitational lensing
12/10/2018 Vislab Richard Galvez (NYU) Artificial Intelligence and Heliophysics
02/04/2019 Vislab Gabriella Contardo (CCA) Unsupervised learning on time-series with CNN Auto-encoders
02/18/2019 Vislab Miles Cranmer Stream Detection using Photometric Distance Posteriors Deep-Learned from Gaia
03/25/2019 140 all Journal Club
04/15/2019 140 Peter Melchior Learning to see like HSC & LSST
04/22/2019 Vislab Evan Schneider Accelerating Scientific Calculations with GPUs
04/29/2019 140 Lucas Makinen Improving Bayesian Hierarchical Modeling for Supernova Cosmology via Selection Effects
05/13/2019 145 Stéphane Mallat (Collège de France) Multiscale Model Reduction in Physics with Deep Networks