AstroInformatics 2019

The final agenda can be found here

Consider participating in the hackathon (open to anyone interested)

Tutorials:

Name

Institution

Tutorial

Dima Duev Caltech Deep Learning
Matthew Graham Caltech Time series analysis
Santiago Lombeyda Caltech Data Visualization
Umaa Rebbapragada JPL Machine Learning basics

The Invited Speakers Include:

Name

Institution

Tentative Title

Anima Anandkumar Caltech Artificial Intelligence Keynote
Bruce Bassett SAAO/UCT Scaling Towards Exabyte Science With The SKA
Andy Connolly DIRAC/UW Looking Below the Noise - Asteroid Hunting With the LSST
Dan Crichton JPL

Enabling Methodology Transfer for Scientific Analysis from Space Science to Biomedicine

Rich Doyle JPL JPL, Autonomy, and Data Science
Francisco Forster CMM/MAS    The Universe in a Stream: Building the ALeRCE Broker
Matthew Graham Caltech

Can We Predict the Future of Aperiodic Sources?

Ajit Kembhavi IUCAA Applications of Deep Learning in Astronomy and Electron Microscopy
Alberto Krone Martins U of Lisbon  Strongly Lensed Quasars: Where Entropy Meets Astrometry, Wavelets And Machine Learning
Ashish Mahabal Caltech The Why And How Of Deep Learning
Jess McIver Caltech Noise Mitigation Methods For Gravitational Wave Detectors
Lior Pachter Caltech High-Dimensional Data Analysis In Astronomy And Biology
Kai Polsterer HITS From Photometric Redshift to Improved Weather Forecasts: An Interdisciplinary View of Machine Learning in Astronomy
John Preskill Caltech Quantum Computing Keynote
Pavlos Protopapas IACS/Harvard

Physical Symmetries Embedded in Neural Networks

Tapio Schneider Caltech/JPL Clouds, Climate, And Data-Informed Earth System Modeling
Peter Tino
U. Birmingham Dynamical Systems as Feature Representations for Learning from Data
Kiri Wagstaff JPL Anomaly Detection And Explanation In Galaxy Observations From The Dark Energy Survey