FI Computational Methods and Data Science Journal Club
Flatiron Institute, 162 5th Avenue
Speaker: Benjamin Wandelt (CCA)
Title: Neural Computation in Bayesian Inference and Applications to Cosmological Data Science
Abstract: Many interesting Bayesian inference problems seem intractable or computationally challenging. Such problems include first level inference (computing posterior pdfs) or second level inference (computing Bayesian evidence) of non-linear, hierarchical models with large numbers of parameters. I will give examples of practical solutions that operate by recasting the computational statistics problem as optimization problems and using neural, differentiable function representations. In many cases, the key ingredient is the ability to forward generate data from the model, e.g. through numerical simulations. When numerical simulations are expensive we can train generative models. For cosmological applications it is often possible to exploit the translational and rotational symmetries of the problem to learn neural generative and conditional models from a single (pair of) training example(s). As we solve these computational problems, new, conceptual challenges come into view: how do we deal with imperfections in our physical models in an observational science such as cosmology? Can we combine ab initio with data-driven approaches? What will be the ultimate limit to cosmological knowledge?
Bio: Professor Wandelt (Ph.D., Imperial College, London) held early career research fellowships at the Theoretical Astrophysics Centre (Niels Bohr Institute) in Copenhagen and at the Department of Physics at Princeton University. In 2001, he became assistant professor in the Departments of Physics and Astronomy at the University of Illinois at Urbana-Champaign, receiving tenure in 2006. In 2010 he was awarded the International Chair of Theoretical Cosmology at Sorbonne University and the Institut d'Astrophysique de Paris. In 2011 he was the founding co-director of the Institut Lagrange de Paris in cosmology, astro-particle, and theoretical physics and was named director in 2014. Professor Wandelt has held long-term visiting faculty positions at the Max Planck Institute for Astrophysics; Caltech; Princeton University; the Institute for Advanced Studies, Princeton; and NYU; and joined the Center for Computational Astrophysics at the Flatiron Institute in New York City in 2017.
Professor Wandelt's research in theoretical, computational, and statistical astrophysics connects fundamental physics and cosmology with astronomical data ranging from stars to the largest scales accessible to observations. He has won multiple awards in several countries such as the Xerox award, the Friedrich Wilhelm Bessel prize, the Sofja Kovalevskaja award, and a senior Excellence Chair of the Agence Nationale de Recherche. He co-led the Primordial Non-Gaussianity analysis for ESA's Planck mission and won multiple prizes as a Planck Scientist and core team member, including the 2018 Gruber Prize in Cosmology. He was elected Fellow of the American Physical Society in 2015 and Fellow of the International Association of Astrostatisticians in 2019. He is the creator of Cosmology@Home, a world-wide participatory computing platform with over 60,000 members.
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