Field-level inference has recently emerged as a powerful alternative to traditional summary-statistic approaches in the analysis of cosmological data sets. This technique exploits the full information content of data from the cosmic microwave background, galaxy redshift surveys, and forthcoming multi-wavelength imaging campaigns, allowing us to extract considerably more information from cosmic surveys compared to traditional analysis methods focused on modeling two-point correlations. This workshop will convene cosmologists, statisticians, machine-learning practitioners, and high-performance-computing experts to accelerate progress on this rapidly evolving frontier.
All sessions will be plenary to maximise cross-disciplinary dialogue, with ample time reserved for structured discussion and collaborative problem-solving.
:: :: ::
Speakers
Adrian Bayer (Flatiron Institute / Princeton University)
Carolina Cuesta-Lazaro (Flatiron Institute)
Natali de Santi (Berkeley)
Adriaan Duivenvoorden (MPA Garching)
Fei Ge (Caltech)*
Yashar Hezaveh (Université de Montréal)
Mikhail Ivanov (MIT)
Jens Jasche (Stockholm University)
Azadeh Moradinezhad (CNRS - LAPTh)
Fabian Schmidt (MPA Garching)
Uros Seljak (University of California, Berkeley)
*Virtual Presenter
Scientific Organizers
Marco Bonici (University of Waterloo)
Neal Dalal (Perimeter Institute)
Beatriz Tucci (Stanford University)