The second edition of the workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics 2022 is open for registration < https://indico.in2p3.fr/event/26887/ >. It will be held in Paris, France the week of June 20th to 24thand a call for contributions is open until May 18th CET.
The goal of this workshop, sponsored by the LSSTC Enabling Science effort – with contributions from DESC, ISSC and TVS collaborations – is to bring together physicists and machine learning specialists to exchange recent results at the crossroads between cosmology, time-domain astrophysics (including gravitational wave astronomy) and probabilistic machine learning frameworks to leverage uncertainties. During registration, young scientists are invited to apply for grants covering lodging and part of the conference fees. Please follow the indications on the registration website for grant applications < https://indico.in2p3.fr/event/26887/page/3006-grant-application-students... >.
The first day of the workshop will be structured as a school to introduce the Bayesian framework and probabilistic machine learning concepts. The rest of the workshop will alternate between keynote talks, topical presentations, interactive tutorials and poster sessions.
We welcome in particular contributions that target, or report on, the following non-exhaustive list of topics:
• Applications of Bayesian Deep Learning in Cosmology and Time Domain Astrophysics
• Methodology for Model Uncertainty Quantification
• Anomaly and outlier detection
• Probabilistic ML frameworks and methodology
• Use of Bayesian deep learning outside of academia
• Ethical considerations of large-scale machine learning
Contributions do not necessarily need to be astrophysics-focused. Work on relevant ML methodology, or similar considerations in other scientific fields are welcome.
Confirmed keynote speakers and panelists
• Anja Butter, ITP Heidelberg, Germany
• Jean-Gabriel Ganascia, LIP6, Paris, France
• Stephen Green, MPI, Potsdam, Germany
• Alan Heavens, Imperial College, London, UK
• Tomasz Kacprzak, ETH Zurich / PSI, Switzerland
• Ashley Villar, Penn State University, USA
• Ben Wandelt, IAP, Paris, France