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Learn the Universe -- an ML X Cosmology Workshop

Aug 24 – 27, 2021
Flatiron Institute
America/New_York timezone

Contribution List

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2. Introduction- Welcome & Outline of the workshop
8/24/21, 10:00 AM
7. Roger de Belsunce: Unbiased inference from large-scale CMB data using likelihood-approximation schemes
8/24/21, 10:15 AM

REMOTE
8. ChangHoon Hahn: Higher-Order LSS with SBI
8/24/21, 10:30 AM
9. Ben Horowitz: hyphy - Conditional Posterior Surrogate Modeling of Hydrodynamical Physics
8/24/21, 10:45 AM

REMOTE
3. Boryana Hadzhiyska: TBD
8/24/21, 11:00 AM
34. Digvijay Wadekar : Symbolic regression for cluster mass estimation
8/24/21, 11:15 AM
4. Unstructured Section: Problems we can't solve with current tools that we may have a shot with ML
8/24/21, 12:15 PM

Chair: Simone Ferraro & Colin Hill
19. Sihao Cheng: The scattering transform in cosmology, or, a CNN without Training
8/24/21, 2:00 PM
20. Kate Storey-Fisher: Emulation of Summary Statistics for Cosmology from Galaxy Surveys
8/24/21, 2:15 PM
21. Agnès Ferté: Categorizing cosmological models with unsupervised learning
8/24/21, 2:30 PM
22. Colin Hill: Non-Gaussian Information in CMB Secondary Anisotropies
8/24/21, 2:45 PM
23. Luisa Lucie-Smith: Deep learning insights into dark matter halo formation
8/24/21, 3:00 PM

REMOTE
6. Unstructured Section: Have non-Gaussian statistics been made redundant?
8/24/21, 4:15 PM

Chair: Zoltan Haiman
10. Francisco Villaescusa-Navarro: The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project
8/25/21, 10:00 AM
11. Siamak Ravanbakhsh: deep Networks for Spherical Data
8/25/21, 10:15 AM
12. Neerav Kaushal: Mapping from Fast Simulations to Full N-body Simulations
8/25/21, 10:30 AM
13. Yin Li: Cosmological Forward Modeling with Adjoint Method
8/25/21, 10:45 AM
14. Leander Thiele: DeepSets applied to Clusters: Machine learning the Lagrangian way
8/25/21, 11:00 AM
15. Biwei Dai: Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
8/25/21, 2:00 PM
16. David Yallup : Principled Bayesian Neural Networks
8/25/21, 2:15 PM
17. Miles Cranmer: Histogram Pooling Operators for Interpretable Deep Learning in Cosmology
8/25/21, 2:30 PM
18. Anima Anandkumar: AI4 Science A revolution in the making
8/25/21, 2:45 PM

REMOTE
35. Michael Eickenberg: Wavelet Modulus Integrals for Parameter Estimation
8/25/21, 3:00 PM
24. Andrina Nicola : Forecasting cosmological and astrophysical constraints from electron-matter cross-correlations
8/26/21, 11:30 AM
25. Christina Kreisch: Precision Cosmology from Voids in the Machine Learning Era
8/26/21, 11:45 AM
26. Ira Wolfson: The Fault In Our Spectrum: A 'no go' on small field models analytics
8/26/21, 12:00 PM
27. Adrian Bayer: The Look-Elsewhere Effect
8/26/21, 12:15 PM
28. Max Lee: Gradient based inference in cosmology using MADLens
8/26/21, 12:30 PM
36. Alina Sabyr: Cosmological Constraints from Weak Lensing Peaks: Can Halo Models Accurately Predict Peak Counts?
8/26/21, 12:45 PM
30. Anima Anandkumar, “ AI4 Science A revolution in the making “
8/26/21, 2:00 PM

REMOTE
29. Greg Bryan, Introducing the Simons Collaboration “Learning the Universe”
8/26/21, 2:15 PM
33. Rachel Somerville: New methods to model galaxy formation so that we can Learn the Universe
8/26/21, 2:30 PM
31. Ana Maria Delgado: Modeling Galaxy-Halo connection with Machine Learning
8/26/21, 2:45 PM
32. Sultan Hassan: HIFlow: Fast Emulator of HI maps using Normalizing Flow
8/26/21, 3:00 PM
1. Unstructured Section: Problems we can't solve with current tools that we may have a shot with ML

Chair: Simone Ferraro & Colin Hill

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