Learn the Universe — an ML X Cosmology Workshop

Date & Time

Learn the Universe — an ML X Cosmology Workshop

Join organizers Shirley Ho, Vanessa Böhm, William Coulton, Elena Giusarma, and Chirag Modi for a workshop on the interdisciplinary work between cosmology and machine learning.

Tuesday, August 24 – Thursday, August 26

Interactive Day on Friday, August 27


  • Location plus--large

    The Center for Computational Astrophysics (CCA) is located at 162 5th Avenue. The entrance to the Flatiron Institute is on 21st Street. 


    Please check in with security upon entering the building; you will need to show your ID and proof of vaccination status for entrance into the building, and please have the health screening questionnaire completed prior to arrival as well.
  • Accommodationsplus--large

    Please NOTE that anyone requesting travel and/or hotel funding will be contacted separately.

    Hotel accommodations will be at the James New York – NoMad Hotel located at 22 E 29th Street between Madison and 5th Avenues: http://www.jameshotels.com/new-york-nomad/.

    Check-in at the hotel is at 2:00 pm. Check-out is at 11:00 am.

    Please email sadika@simonsfoundation.org or dolchick@simonsfoundation.org for the reservation link.

    Travel arrangements must be made by Monday, August 16th.


  • Participantsplus--large
    Abhishek ManiyarNYU
    Adrian BayerUC Berkeley
    Agnes FerteJet Propulsion Laboratory
    Aleksandra K KusiakColumbia University
    Alex KimLBNL
    Alice PisaniPrinceton University
    Alina SabyrColumbia University
    Ana Maria DelgadoCenter for Astrophysics, Harvard and Smithsonian
    Andrina NicolaPrinceton University
    Andy GouldingPrinceton University
    Anima AnandkumarCaltech
    Benjamin HorowitzPrinceton University
    Biwei DaiUC Berkeley
    Boryana HadzhiyskaHarvard (CfA)
    Brian LuColumbia University
    Brice MénardJohns Hopkins University
    ChangHoon HahnPrinceton University
    Chirag ModiCenter for Computational Astrophysics
    Christina KreischPrinceton University
    Colin HillFlatiron / Columbia
    David W. HoggFlatiron Institute
    David SpergelSimons Foundation
    David YallupUniversity of Cambridge
    Dongwon HanStony Brook University
    Elena GiusarmaMichigan Tech University
    Francisco Villaescusa-NavarroPrinceton University
    Francois LanusseCNRS
    Gemma MoranColumbia University
    George SteinUC Berkeley
    Giulio FabbianCCA and Cardiff University
    Huanqing ChenUniversity of Chicago
    Ira WolfsonSISSA (Scuola Internazionale Superiore di Studi Avanzati)
    Julia KempeNYU
    Kate Storey-FisherNYU
    Kaze WongFlatiron Institute
    Lars HernquistHarvard University
    Laurence Perreault-LevasseurUniversity of Montreal
    Leander ThielePrinceton University
    Lehman GarrisonCCA
    Luisa Lucie-SmithMax Planck Institute for Astrophysics
    Matias ZaldarriagaIAS
    Max LeeUC Berkeley
    Michael RashkovetskyiCenter for Astrophysics | Harvard & Smithsonian
    Miles CranmerPrinceton University
    Neerav KaushalMichigan Technological University
    Rachel SomervilleCCA
    Rajib Rezwan ChowdhuryUniversity of Central Florida
    Roger de BelsunceUniversity of Cambridge
    Shy GenelCCA
    Siamak RavanbakhshMcGill - Mila
    Sihao ChengJohns Hopkins University
    Simone FerraroLawrence Berkeley National Laboratory
    Stephon AlexanderBrown University
    Sultan HassanCCA
    Syed Muntazir Mehdi AbidiUniversity of Geneva
    Tanveer KarimHarvard University
    Uros SeljakUC Berkeley
    Vanessa BoehmUC Berkeley
    Wenda ZhouNYU CDS
    Will HandleyUniversity of Cambridge
    William CoultonCCA
    Xiaohan WuHarvard CfA
    Yin LiFlatiron Institute
    Yun-Ting ChengCaltech
    Zoltan HaimanColumbia University
  • Agenda plus--large

    10:00am-10:15am Introduction- Welcome & Outline of the Workshop
    10:15am-11:15am structured section: Simulations Based Inference
    Chair: Francisco Villaescusa-Navarro

    1. Roger de Belsunce: Unbiased inference from large-scale CMB data using likelihood-approximation schemes
    2. ChangHoon Hahn: Higher-Order LSS with SBI
    3. Ben Horowitz: hyphy – Conditional Posterior Surrogate Modeling of Hydrodynamical Physics
    4. Boryana Hadzhiyska: TBD
    5. Digvijay Wadekar : Symbolic regression for cluster mass estimation

    11:45am-12:15pm Coffee
    12:15pm-1:00 pm unstructured section: Problems we can’t solve with current tools that we may have a shot with ML
    Chair: Simone Ferraro and Colin Hill
    1:00pm-2:00pm: Lunch
    2:00pm-3:45pm: structured section: Extracting Non-Gaussian Information
    Chair: Andrina Nicola

    1. Sihao Cheng: The scattering transform in cosmology, or, a CNN without Training
    2. Kate Storey-Fisher: Emulation of Summary Statistics for Cosmology from Galaxy Surveys
    3. Agnès Ferté: Categorizing cosmological models with unsupervised learning
    4. Colin Hill: Non-Gaussian Information in CMB Secondary Anisotropies
    5. Luisa Lucie-Smith: Deep learning insights into dark matter halo formation

    3:45pm-4:15pm: Coffee
    4:15pm-5:00pm: unstructured section: Have non-Gaussian statistics been made redundant?
    Chair: Zoltan Haiman
    10:00am-11:45am structured section: Simulation X ML
    Chair: Sultan Hassan

    1. Francisco Villaescusa-Navarro: The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project
    2. Siamak Ravanbakhsh: Deep Networks for Spherical Data
    3. Neerav Kaushal: Mapping from Fast Simulations to Full N-body Simulations
    4. Yin Li: Cosmological Forward Modeling with Adjoint Method
    5. Leander Thiele: DeepSets applied to Clusters: Machine learning the Lagrangian way

    11:45am-12:15pm Coffee
    12:15pm-1:00pm: unstructured section: Pitch your Astro challenge in 5 mins OR Pitch your method in 5 mins
    (participants can prepare a slide or two for this session)
    Chair: ChangHoon Han and Miles Cranmer
    1:00pm-2:00pm: Lunch
    2:00pm-3:45pm: structured section: Robust ML for science
    Chair: Yin Li

    1. Biwei Dai: Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
    2. David Yallup : Principled Bayesian Neural Networks
    3. Miles Cranmer: Histogram Pooling Operators for Interpretable Deep Learning in Cosmology

    3:45pm-4:15pm: Coffee
    4:15pm-5:00pm: unstructured section: ML 4 Science – Promises and Problems
    Chair: Viviana Acquaviva
    10:00am-11:00am unstructured section: The next Decade in Cosmology
    Chair: David Spergel
    11:00am-11:30am Coffee
    11:30am-1:00pm: structured section: Cosmological Applications
    Chair: Alice Pisani

    1. Andrina Nicola : Forecasting cosmological and astrophysical constraints from electron-matter cross-correlations
    2. Christina Kreisch: Precision Cosmology from Voids in the Machine Learning Era
    3. Ira Wolfson: The Fault In Our Spectrum: A ‘no go’ on small field models analytics
    4. Adrian Bayer: The Look-Elsewhere Effect
    5. Max Lee: Gradient based inference in cosmology using MADLens
    6. Alina Sabyr: Cosmological Constraints from Weak Lensing Peaks: Can Halo Models Accurately Predict Peak Counts?

    1:00pm-2:00pm: Lunch
    2:00pm-3:45pm: structured section: Simons Collaboration “Learning the Universe”
    Chair: Shirley Ho

    1.  Greg Bryan, Introducing the Simons Collaboration “Learning the Universe”
    2. Rachel Somerville: New methods to model galaxy formation so that we can Learn the Universe
    3. Ana Maria Delgado: Modeling Galaxy-Halo connection with Machine Learning
    4. Sultan Hassan: HIFlow: Fast Emulator of HI maps using Normalizing Flow.

    3:45-4:15 pm: Coffee
    4:15pm-5:00pm: unstructured section: What qualifies as interpretability? Do we need it?
    Chair: Julia Kempe and Vanessa Bohm


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