Name: ML-IAP/CCA-2023
Start: 2023-11-27T14:00:00+01:00
End: 2023-12-01T18:30:00+01:00
Location: Dual node

Monday, November 27, 2023

2:00 PM Registration
Registration
2:00 PM - 2:45 PM

2:45 PM Welcome
Welcome
2:45 PM - 3:00 PM
Room: IAP Amphitheatre

3:00 PM Introduction
Introduction
3:00 PM - 3:15 PM
3:15 PM Block 1
Block 1
3:15 PM - 4:15 PM
Contributions

3:15 PM Field-Level Inference with Microcanonical Langevin Monte Carlo - Adrian Bayer (Princeton University / Simons Foundation)

3:30 PM Current progress and challenges from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project - Daniel Angles-Alcazar (University of Connecticut)

3:45 PM Data Compression and Inference in Cosmology with Self-Supervised Machine Learning - Aizhan Akhmetzhanova (Harvard University)

4:00 PM DeepSZSim: Fast Simulations of the Thermal Sunyaev–Zel’dovich Effect in Galaxy Clusters for Simulation-based Inference - Eve Vavagiakis (Cornell University)

4:06 PM Probing primordial non-Gaussianity by reconstructing the initial conditions with convolutional neural networks - Xinyi Chen (Yale University)

4:09 PM Simulation-based inference with non Gaussian statistics in the Dark Energy Survey - Marco Gatti (UPenn)
4:15 PM Coffee break
Coffee break
4:15 PM - 4:45 PM

Poster loop
Poster loop
4:15 PM - 4:45 PM
4:45 PM Block 2
Block 2
4:45 PM - 6:30 PM
Contributions

4:45 PM SNAD: enabling discovery in the era of big data - Maria Pruzhinskaya

5:00 PM DE-VAE: a representation learning architecture for a dynamic dark energy model - Davide Piras (University of Geneva)

5:15 PM A Bayesian Neural Network based ILC method to estimate accurate CMB polarization power spectrum over large angular scales - Sarvesh Kumar Yadav (Raman Research Institute, Banglore, India)

5:18 PM Multiview Symbolic Regression in astronomy - Etienne Russeil (Laboratoire de Physique de Clermont (LPC))

5:21 PM Machine learning for new physics - Agnès Ferté (SLAC/Stanford U)

5:36 PM Toward automated discovery of analytical physical laws from data using deep reinforcement learning - Wassim TENACHI (Observatoire Astronomique de Strasbourg)

5:51 PM Latent space out-of-distribution detection of galaxies for deblending in weak lensing surveys - Jelle Mes (Leiden Observatory, Leiden University, The Netherlands)

5:54 PM Fishnets: Mapping Information Geometry with Robust, Scalable Neural Compression - Benjamin Wandelt (Institut d'Astrophysique de Paris / The Flatiron Institute) Lucas Makinen (Imperial College London)

5:57 PM Extracting physical rules from ensemble machine learning for the selection of radio AGN. - Rodrigo Carvajal

6:00 PM Opportunities and challenges of machine learning for astrophysics - Jason McEwen (UCL)

6:03 PM The terms Eisenstein and Hu missed - Deaglan Bartlett (Institut d'Astrophysique de Paris)
7:00 PM Welcome Cocktail
Welcome Cocktail
7:00 PM - 10:45 PM
8:00 PM
8:00 PM - 9:00 PM
Contributions

8:00 PM TBD: Deep learning and numerical simulations - Rupert Croft
9:30 PM Reception
Reception
9:30 PM - 11:30 PM
Tuesday, November 28, 2023
10:00 AM
10:00 AM - 11:00 AM
Room: Amphitheater
Contributions

10:00 AM Generative models to assist sampling - Marylou Gabrié (CMAP, Ecole Polytechnique)
11:00 AM Coffee break
Coffee break
11:00 AM - 11:30 AM
Room: Hall

Poster loop
Poster loop
11:00 AM - 11:30 AM
11:30 AM
11:30 AM - 12:30 PM
Room: Amphitheater
Contributions

11:30 AM TBD: Deep learning and numerical simulations (Replay) - Rupert Croft
3:00 PM Block 3
Block 3
3:00 PM - 3:54 PM
Contributions

3:00 PM Field-level inference of primordial non-Gaussianity, using next-generation galaxy surveys - Adam Andrews (INAF OAS Bologna)

3:15 PM SBI meets reality: simulation-based inference in practical cosmology applications - Benjamin Joachimi (University College London)

3:18 PM Sampling with Hamiltonian Neural Networks - Vincent Souveton (LMBP - Université Clermont Auvergne)

3:21 PM The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues - Lucas Makinen (Imperial College London)

3:24 PM Reconstruction of cosmological initial conditions with sequential simulation-based inference - Oleg Savchenko (University of Amsterdam)

3:27 PM EFTofLSS meets simulation-based inference: $\sigma_8$ from biased tracers - Beatriz Tucci (Max Planck Institute for Astrophysics)

3:30 PM Field-level Emulator within Bayesian Origin Reconstruction from Galaxies (BORG) - Ludvig Doeser (Stockholm University)

3:36 PM Dealing with systematic effects: the issue of robustness to model misspecification - Florent Leclercq (Institut d'Astrophysique de Paris)

3:39 PM Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets - Andrea Roncoli (Fermi National Accelerator Laboratory)
3:54 PM Coffee break
Coffee break
3:54 PM - 4:23 PM

Poster loop
Poster loop
3:54 PM - 4:23 PM
4:23 PM Block 4
Block 4
4:23 PM - 5:30 PM
Contributions

4:23 PM Galaxy modeling with physical forward models and generative neural networks - Peter Melchior (Princeton University)

4:38 PM Neutrino mass constraint from an Implicit Likelihood Analysis of BOSS voids - Leander Thiele (Princeton University)

4:45 PM Machine learning cosmology from void properties - Bonny Y. Wang (Flatiron Institute / Carnegie Mellon University)

4:52 PM Harnessing Differentiable and Probabilistic Programming for Scalable and Robust Statistical Analysis of Astronomical Surveys - Alessio Spurio Mancini (University College London)

4:55 PM A Reanalysis of BOSS Galaxy Clustering Data with a Simulation-Based Emulator of the Wavelet Scattering Transform - Georgios Valogiannis

4:58 PM A neural-network emulator for the Lyman-$\alpha$ flux power spectrum - Laura Cabayol-Garcia (IFAE/PIC)

5:01 PM Towards an Astronomical Foundation Model for Stars with a Transformer-based Model - Henry Leung (University of Toronto)

5:16 PM Before real data: pressing graph neural networks to do field-level simulation-based inference with galaxies - Natalí Soler Matubaro de Santi (University of São Paulo)

5:23 PM Modeling galaxy orientations on the SO(3) manifold with score-based generative models - Yesukhei Jagvaral (Carnegie Mellon University)
5:30 PM Break
Break
5:30 PM - 6:00 PM

Poster loop
Poster loop
5:30 PM - 6:00 PM

6:00 PM Debate 1: Is there truth in latent space?
Debate 1: Is there truth in latent space?
6:00 PM - 7:30 PM
9:00 PM
9:00 PM - 10:00 PM
Contributions

9:00 PM TBD: Domain adaptation - David Shih (Rutgers University)
10:00 PM Coffee break
Coffee break
10:00 PM - 10:30 PM

Poster loop
Poster loop
10:00 PM - 10:30 PM
10:30 PM
10:30 PM - 11:30 PM
Contributions

10:30 PM Generative models to assist sampling - Marylou Gabrié (CMAP, Ecole Polytechnique)
Wednesday, November 29, 2023
10:00 AM
10:00 AM - 11:00 AM
Contributions

10:00 AM TBD: Symbolic regression - Miles Cranmer (Cambridge University)
11:00 AM Coffee break
Coffee break
11:00 AM - 11:30 AM
11:30 AM
11:30 AM - 12:30 PM
Contributions

11:30 AM TBD: ML and Bayesian inference in cosmology - Jens Jasche (Stockholm University)
3:00 PM Block 5
Block 5
3:00 PM - 4:01 PM
Contributions

3:00 PM Prioritising Follow-up for Transient Surveys in the New Era of Time-Domain Astronomy - Daniel Muthukrishna (Massachusetts Institute of Technology)

3:15 PM Convolutional Neural Networks for Exoplanet Detection in Photometric Light Curves From Massive Data Surveys - Stela Ishitani Silva (NASA GSFC)

3:22 PM Vision Transformers for Cosmological Inference from Weak Lensing - Shubh Agrawal (University of Pennsylvania)

3:25 PM TheLastMetric: ML for statistically rigorous observing strategy optimization - Alex Malz (Carnegie Mellon University)

3:28 PM Deconstructing the galaxy merger sequence with machine vision - Robert Bickley (University of Victoria)

3:31 PM Doing More With Less; Label-Efficient Learning for Euclid and Rubin - Mike Walmsley (University of Toronto)

3:46 PM Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation - Gemma Zhang (Harvard University)
4:01 PM Coffee break
Coffee break
4:01 PM - 4:30 PM
4:30 PM Block 6
Block 6
4:30 PM - 5:30 PM
Contributions

4:30 PM Deep Learning Generative Models to Infer Mass Density Maps from SZ, X-ray and Galaxy Members Observations in Galaxy Clusters - Daniel de Andrés (Universidad Autónoma de Madrid)

4:45 PM Explaining dark matter halo abundance with interpretable deep learning - Ningyuan (Lillian) Guo (University College London)

5:00 PM Perturbation theory emulator for cosmological analysis - Svyatoslav Trusov (LPNHE)

5:03 PM Debating the Benefits of Differentiable Cosmological Simulators for Weak Lensing Full-Field Inference (LSST Y10 case study) - Justine Zeghal (APC, CNRS)

5:06 PM Fast realistic, differentiable, mock halo generation for wide-field galaxy surveys - Simon Ding (Institut d'Astrophysique de Paris (IAP))

5:09 PM Emulating the Universe: overcoming computational roadblocks with Gaussian processes - Benjamin Giblin (University of Edinburgh)

5:12 PM Who threw that rock? Tracing the path of martian meteorites back to the crater of origin using ML - Konstantinos Servis-Nussbaum (Pawsey/CSIRO)

5:27 PM Investigations for LSST with Machine Learning: Photometric redshift predictions, strong lens detection and mass modeling - Stefan Schuldt
5:30 PM Break
Break
5:30 PM - 6:00 PM

6:00 PM Debate 2: What can machine-learning do for the next generation surveys?
Debate 2: What can machine-learning do for the next generation surveys?
6:00 PM - 7:30 PM

8:30 PM Conference dinner
Conference dinner
8:30 PM - 11:30 PM
9:00 PM
9:00 PM - 10:00 PM
Contributions

9:00 PM TBD: ML and Bayesian inference in cosmology (Replay) - Jens Jasche (Stockholm University)
10:00 PM Coffee break
Coffee break
10:00 PM - 10:30 PM
10:30 PM
10:30 PM - 11:30 PM
Contributions

10:30 PM TBD: Symbolic regression (Replay) - Miles Cranmer (Cambridge University)
Thursday, November 30, 2023
10:00 AM
10:00 AM - 11:00 AM
Contributions

10:00 AM Capitalizing on Artificial Intelligence for LSS Cosmology - Tomasz Kacprzak (ETH Zurich/ Swiss Data Science Center)
11:00 AM Coffee break
Coffee break
11:00 AM - 11:30 AM
11:30 AM
11:30 AM - 12:30 PM
Contributions

11:30 AM TBD: Domain adaptation (Replay) - David Shih (Rutgers University)
3:00 PM Block 7
Block 7
3:00 PM - 4:05 PM
Contributions

3:00 PM Causal graphical models for galaxy surveys - Serafina Di Gioia (ICTP)

3:15 PM Efficient and fast deep learning approaches to denoise large radioastronomy line cubes and to emulate sophisticated astrophysical models - Lucas Einig (Institut de Radioastronomie Millimétrique)

3:30 PM CNNs reveal crucial degeneracies in strong lensing subhalo detection - Conor O'Riordan (Max Planck Institute for Astrophysics)

3:37 PM Selection functions of strong lens finding neural networks - Aniruddh Herle (Max-Planck Institute for Astrophysics, + LMU)

3:44 PM Extending the Reach of Gaia DR3 with Self-Supervision - Aydan McKay (University of Victoria)

3:59 PM ChatGaia - Nolan Koblischke (University or Toronto)
4:05 PM Coffee break
Coffee break
4:05 PM - 4:30 PM
4:30 PM Block 8
Block 8
4:30 PM - 5:33 PM
Contributions

4:30 PM HySBI - Hybrid Simulation-Based Inference - Chirag Modi (Flatiron Institute)

4:45 PM Machine Learning Powered Inference in Cosmology - Pablo Lemos (Mila - Université de Montréal)

5:00 PM Cosmology with Galaxy Photometry Alone - ChangHoon Hahn (Princeton University)

5:15 PM Cosmological constraints from HSC survey first-year data using deep learning - Zoltan Haiman (Columbia University)

5:30 PM Assessing and Benchmarking the Fidelity of Posterior Inference Methods for Astrophysics Data Analysis - Becky Nevin (Fermilab)
5:33 PM Break
Break
5:33 PM - 6:00 PM

6:00 PM Debate 3: What is the impact of large language models in astronomy?
Debate 3: What is the impact of large language models in astronomy?
6:00 PM - 7:30 PM
8:30 PM
8:30 PM - 9:30 PM
Contributions

8:30 PM TBD: Symmetries in deep learning - Soledad Villar
10:00 PM
10:00 PM - 11:00 PM
Contributions

10:00 PM Capitalizing on Artificial Intelligence for LSS Cosmology (replay) - Tomasz Kacprzak (ETH Zurich/ Swiss Data Science Center)
Friday, December 1, 2023
10:00 AM
10:00 AM - 11:00 AM
Contributions

10:00 AM TBD: Symmetries in deep learning (Replay) - Soledad Villar
11:15 AM Seminar at IAP:
Seminar at IAP:
11:15 AM - 12:15 PM
Contributions

11:15 AM Deep learning algorithms for morphological classification of galaxies - Helena Domínguez Sánchez
3:00 PM Block 9
Block 9
3:00 PM - 4:01 PM
Contributions

3:00 PM Data-driven galaxy morphology at $z>3$ with contrastive learning and cosmological simulations - Jesús Vega Ferrero (Universidad de Valladolid (UVa))

3:15 PM Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology - Sut Ieng Tam (Institute of Astronomy and Astrophysics, Academia Sinica (ASIAA))

3:30 PM Optimizing Galaxy Sample Selections for Weak Lensing Cluster Cosmology - Markus Rau (Argonne National Laboratory)

3:33 PM Embedding Neural Networks in ODEs to Learn Linear Cosmological Physics - James Sullivan (UC Berkeley)

3:36 PM Finding Observable Environmental Measures of Halo Properties using Neural Networks - Haley Bowden (University of Arizona)

3:39 PM Machine learning as a key component in the science processing pipelines of space- and ground-based surveys? - Jeroen Audenaert (MIT)

3:46 PM Machine-directed gravitational-wave counterpart discovery - Niharika Sravan (Drexel University)
4:01 PM Coffee break
Coffee break
4:01 PM - 4:30 PM
4:30 PM Block 10
Block 10
4:30 PM - 6:00 PM
Contributions

4:30 PM Spatially Variant Point Spread Functions for Bayesian Imaging - Vincent Eberle (Max Planck Institute for Astrophysics / Faculty of Physics, Ludwig-Maximilians-Universität München (LMU))

4:45 PM Generative Topographic Mapping for tomographic redshift estimates - Grégoire Aufort (IAP)

5:00 PM Reionisation time fields reconstruction from 21 cm signal maps - Julien Hiegel (Observatoire Astronomique de Strasbourg - CNRS)

5:15 PM Significance Mode Analysis (SigMA) for hierarchical structures - Sebastian Ratzenböck (University of Vienna)

5:30 PM Anomaly detection using local measures of uncertainty in latent representations - Fiona Porter (Jodrell Bank Centre for Astrophysics)

5:45 PM Scientific Discovery from Ordered Information Decomposition - Matthew Ho (Institut d'Astrophysique de Paris)
6:00 PM Concluding remarks
Concluding remarks
6:00 PM - 6:30 PM
Contributions

6:00 PM Concluding remarks - Licia Verde