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.
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Speakers
Carolina Cuesta-Lazaro (Flatiron Institute)
Mikhail Ivanov (MIT)
Azadeh Moradinezhad (CNRS - LAPTh)
Fabian Schmidt (MPA Garching)
Uros Seljak (University of California, Berkeley)
... more to be confirmed
Scientific Organizers
Marco Bonici (University of Waterloo)
Neal Dalal (Perimeter Institute)
Beatriz Tucci (Stanford University)
