Monday, October 18, 2021

Time Event (+)
08:45 - 09:45 In person Registration - Registration of in person participants  
09:45 - 10:00 Welcome speech (Henri Mineur amphitheater / Plenary online session) - Organizers  
10:00 - 11:00 Review (Henri Mineur amphitheater / Plenary online session) (+)  
10:00 - 11:00 › Review on cosmology and machine learning - Benjamin Wandelt, Institut d'Astrophysique de Paris  
11:00 - 11:20 Break (Breakout rooms)  
11:20 - 12:20 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
11:20 - 11:35 › Probabilistic Mapping of Dark Matter by Neural Score Matching - Benjamin Remy, Cosmostat, AIM, IRFU, CEA-Saclay  
11:35 - 11:50 › Simulation and Segmentation with Deep Learning for Euclid - Hubert Bretonnière, Institut d'astrophysique spatiale  
11:50 - 12:05 › Search for galaxy-scale strong lenses in DES and CFIS - Frédéric Courbin, EPFL, Laboratoire d'astrophysique, Ecole Polytechnique Fed ´ erale de Lausanne  
12:05 - 12:20 › Learning from 3D tomographic 21cm maps - Caroline Heneka, Hamburg Observatory, Hamburg University  
15:00 - 15:20 Flash talk (Henri Mineur amphitheater / Plenary online session) (+)  
15:00 - 15:04 › Unifying large-scale spectroscopy, astrometry, and photometry with Convolutional Neural-Networks. - Guillaume Guiglion, Leibniz-Institut für Astrophysik Potsdam  
15:04 - 15:08 › Unknown Unknowns: Hybrid machine learning and template based photometric redshifts - Peter Hatfield - University of Oxford  
15:08 - 15:12 › A Deep Learning Neural Network for Voigt Profile Fitting Quasar Absorption Lines - Bryson Stemock - New Mexico State University  
15:12 - 15:16 › Accelerating the modeling of HI on cosmological scales via Deep Learning - Robert Feldmann - University of Zürich [Zürich]  
15:16 - 15:20 › Beyond the Hubble Sequence - Exploring galaxy morphology with unsupervised machine learning - Ting-Yun Cheng, Durham University  
15:20 - 16:05 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
15:20 - 15:35 › Fast and realistic large-scale structure from machine-learning-augmented random field simulations - Davide Piras, UCL  
15:30 - 16:05 › Parameter Estimation with Physics Informed Neural Networks - Alex Lague, University of Toronto, Canadian Institute for Theoretical Astrophysics (CITA)  
15:35 - 15:50 › A new catalog of 343,000 quasars with their photometric redshifts derived with machine learning from the Kilo Degree Survey - Szymon Nakoneczny, National Centre for Nuclear Research [Otwock]  
16:05 - 16:25 Break (Breakout rooms)  
16:25 - 17:25 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
16:25 - 16:40 › Machine learning-infused cluster cosmology - Stéphane Ilic, AstroParticule et Cosmologie, Laboratoire dÉtude du Rayonnement et de la Matière en Astrophysique, Institut de recherche en astrophysique et planétologie  
16:40 - 16:55 › CosmoPower: deep learning emulation of cosmological power spectra for accelerated Bayesian inference from next-generation surveys - Alessio Spurio Mancini, University College, London  
16:55 - 17:10 › Single frequency CMB polarized foreground marginalization with a single training image - Niall Jeffrey, École normale supérieure  
17:10 - 17:25 › Bayes vs. machine learning in large-scale survey demographics with eROSITA - Johannes Buchner, Max Planck Institute for Extraterrestrial Physics  
17:25 - 18:00 Break (Breakout rooms)  
18:00 - 19:30 Debate (Henri Mineur amphitheater / Plenary online session)  
19:30 - 22:30 Cocktail dinner - Cocktail of the conference in Cassini room for in person participants - Cocktail dinner  

Tuesday, October 19, 2021

Time Event (+)
10:00 - 11:00 Review (Henri Mineur amphitheater / Plenary online session) (+)  
10:00 - 11:00 › Imaging Surveys and ML - Elisabeth Krause, University of Arizona  
11:00 - 11:20 Break (Breakout rooms)  
11:20 - 12:20 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
11:20 - 11:35 › Source detection through semantic segmentation with convolutional neural networks - Maxime Paillassa, Laboratoire dÁstrophysique de Bordeaux [Pessac], Division of Physics and Astrophysical Science, Graduate School of Science, Nagoya University  
11:35 - 11:50 › Identifying strong gravitational lenses in current and future large-scale imaging surveys - Raoul Canameras, MPA, Garching  
11:50 - 12:05 › Reconstructing blended galaxies with Machine Learning - Lavanya Nemani, INAF - Osservatorio Astronomico di Roma  
12:05 - 12:20 › Pushing automated morphological classifications to their limits with the Dark Energy Survey - Jesús Vega Ferrero, Instituto de Astrofisica de Canarias  
15:00 - 15:20 Flash talk (Henri Mineur amphitheater / Plenary online session) (+)  
15:00 - 15:04 › Rediscovering Newton's gravity and Solar System properties using deep learning and inductive biases - Pablo Lemos, Department of Physics and Astronomy [UCL London], Department of physics and Astronomy [University of Sussex]  
15:04 - 15:08 › Gaussian Process Regression: An Application to Radio Cosmology - Paula Soares, Queen Mary, University of London  
15:08 - 15:12 › Detection of the Damped Lyman-alpha systems in quasar spectra with machine learning algorithms - ting tan, Laboratoire de Physique Nucléaire et de Hautes Énergies  
15:12 - 15:16 › Forecasting the power of Higher Order Weak Lensing Statistics with automatically differentiable simulations - Denise lanzieri, CEA-Saclay  
15:16 - 15:20 › Manifold learning to explore the galaxy parameter space - Iary Davidzon, DAWN/NBI - Keerthana Jegatheesan, Universidad Diego Portales - Olivier Ilbert, LAM - Clotilde Laigle, IAP  
15:20 - 16:05 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
15:20 - 15:35 › Machine learning within the THREEHUNDERD simulation project - Daniel de Andres, Universidad Autonoma de Madrid  
15:35 - 15:50 › Superresolving Herschel imaging: a proof of concept using Deep Neural Networks - Lynge Lauritsen, The Open University  
15:50 - 16:05 › Capturing the physics of MaNGA galaxies with self-supervised Machine Learning - Regina Sarmiento, Universidad de La Laguna [Tenerife - SP], Instituto de Astrofisica de Canarias  
16:05 - 16:25 Break (Breakout rooms)  
16:25 - 17:40 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
16:25 - 16:40 › ‘Deep' vs. 'Shallow' Learning in Cosmological Surveys - Ofer Lahav, University College London  
16:40 - 16:55 › A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with t-SNE - John Weaver, Cosmic Dawn Center  
16:55 - 17:10 › From machine learning to human understanding - Daniel Masters, Caltech / IPAC  
17:10 - 17:25 › Use data not models -- Lensing of '69 as an example for data-driven inference - Jenny Wagner, https://thegravitygrinch.blogspot.com  
17:25 - 17:40 › Machine learning: lessons learnt with the QUBRICS survey - Francesco Guarneri, Astronomical Observatory of Trieste, Dipartimento di Fisica [Trieste]  
17:00 - 18:00 Break (Breakout rooms)  
18:00 - 19:30 Debate (Henri Mineur amphitheater / Plenary online session)  

Wednesday, October 20, 2021

Time Event (+)
15:00 - 16:00 Review (Henri Mineur amphitheater / Plenary online session) (+)  
15:00 - 16:00 › Review on generative models in Machine Learning - Gilles Loupe, Universite de Liege  
16:00 - 16:20 Break (Breakout rooms)  
16:20 - 16:40 Flash talk (Henri Mineur amphitheater / Plenary online session) (+)  
16:20 - 16:24 › Studying Morphology & Quenching of Galaxies in the All Sky-Era using Interpretable Bayesian Convolutional Neural Networks - Aritra Ghosh, Yale University  
16:28 - 16:32 › Emulating the void density profile and the void-size function - Giorgia Pollina, Universitäts-Sternwarte, Ludwig-Maximilians Universität München  
16:32 - 16:36 › Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language 'captioning' model - Michael Smith - University of Hertfordshire, Hatfield, UK, Queen's University, Kingston, ON, Canada  
16:36 - 16:40 › Bayesian Inference Acceleration: a Deep Learning Approach - Marco Bonici, Istituto di Astrofisica Spaziale e Fisica Cosmica - Milano  
16:40 - 17:40 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
16:40 - 16:55 › CosmicRIM : Reconstructing Early Universe by Combining Differentiable Simulations with Recurrent Inference Machines - Chirag Modi, Flatiron Institute  
16:55 - 17:10 › Bridging the gap between simulations and survey data - domain adaptation for deep learning in astronomy - Aleksandra Ciprijanovic, Fermi National Accelerator Laboratory  
17:10 - 17:25 › Stratified Learning: A general-purpose method for learning under covariate shift with applications to observational cosmology - Roberto Trotta, Department of Physics [Imperial College London], Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies  
17:25 - 17:40 › Neural spectrum encoding - Peter Melchior, Princeton University  
17:40 - 18:00 Coffee break (Breakout rooms)  
18:00 - 19:30 Debate (Henri Mineur amphitheater / Plenary online session)  

Thursday, October 21, 2021

Time Event (+)
10:00 - 11:00 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
10:00 - 10:15 › The Dark Quest project for cosmological emulation - Takahiro Nishimichi, Kavli Institute for the Physics and Mathematics of the Universe [Tokyo], Yukawa Institute for Theoretical Physics  
10:15 - 10:30 › The scattering transform in cosmology, or, a CNN without Training - Sihao Cheng, Johns Hopkins University  
10:30 - 10:45 › Considerations for optimizing photometric classification of supernovae from the Rubin Observatory - Catarina S. Alves, University College, London  
10:45 - 11:00 › Machine Learning Calibration of Cosmic Shear Redshift Distributions - Angus Wright, Ruhr University Bochum  
11:00 - 11:20 Interaction session (Breakout rooms)  
11:20 - 12:05 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
11:20 - 11:35 › LyAl-Net: A high-efficiency Lyman-α forest simulation with neural network - Chotipan Boonkongkird, Sorbonne Universite  
11:35 - 11:50 › Dark Substructure Sensitivity in the Euclid Survey with Machine Learning - Conor O'Riordan, Max Planck Institute for astrophysics  
11:50 - 12:05 › Inferring the assembly and merger histories of galaxies with the IllustrisTNG simulations and machine learning - Lukas Eisert, Max Planck Institute for Astronomy  
12:05 - 12:25 Flash talk (Henri Mineur amphitheater / Plenary online session) (+)  
12:05 - 12:09 › Applying Deep Neural Network to dark matter halo catalogues to constrain the dark energy equation-of-state parameters - Farida Farsian, INAF - Osservatorio Astronomico di Bologna, Alma Mater Studiorum University of Bologna  
12:09 - 12:13 › Cosmological constraints from galaxy-galaxy lensing and galaxy clustering with HSC-Y1 and BOSS data: the first application of emulator-based halo model to cosmology analysis - Hironao Miyatake, Kavli Institute for the Physics and Mathematics of the Universe [Tokyo], Nagoya University  
12:13 - 12:17 › Tidal stream detection in HSC-SSP with Deep Learning - Helena Domínguez Sánchez, Institute of Space Sciences [Barcelona]  
12:17 - 12:21 › Using convolutional neural networks to identify strong lenses in Euclid and J-PAS - Alberto Manjon García, Instituto de Física de Cantabria  
12:21 - 12:25 › Galaxy Cluster Mass Estimation Using Deep Learning - Matthew Ho, Carnegie Mellon University  
15:00 - 16:00 Review (Henri Mineur amphitheater / Plenary online session) (+)  
15:00 - 16:00 › Review on machine learning and cosmological simulations - Francisco Villaescusa-Navarro, Flatiron Institute, Department of Astrophysical Sciences [Princeton]  
16:00 - 16:20 Coffee break (Breakout rooms)  
16:20 - 16:40 Flash talk (Henri Mineur amphitheater / Plenary online session) (+)  
16:20 - 16:24 › Photometric redshift calibration with simulated annealing and self-organising maps - Benjamin Stölzner, University College, London  
16:24 - 16:28 › Semi-Supervised Learning for Probabilistic Photometric Redshifts - Rory Coles, National Research Council of Canada, Herzberg Astronomy & Astrophysics Program, University of Victoria  
16:28 - 16:32 › Time Delay Estimation in Unresolved Lensed Quasars via Convolutioanl Neural Networks - Luca Biggio, ETH - Alba Domi, University of Genoa - Luca Paganin, University of Genoa  
16:32 - 16:36 › Detection of the Damped Lyman-alpha systems in quasar spectra with machine learning algorithms - ting tan, Laboratoire de Physique Nucléaire et de Hautes Énergies  
16:35 - 17:20 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
16:35 - 16:50 › Real-time science-driven follow-up of survey transients: Optimally augmenting ZTF SN Ia light curves for maximizing SALT2 parameter constraints - Niharika Sravan - Caltech  
16:50 - 17:05 › What drives the scatter in the BPT diagrams? A Machine Learning based analysis - Mirko Curti - Kavli Institute for Cosmology [Cambridge]  
17:05 - 17:20 › Quantifying uncertainty in deep-learning classification of radio galaxies - Fiona Porter - Jodrell Bank Centre for Astrophysics  
17:20 - 18:00 Coffee break (Breakout rooms)  
18:00 - 19:30 Debate (Henri Mineur amphitheater / Plenary online session)  
20:30 - 23:00 Conference Dinner - Conference dinner pour in person participation  

Friday, October 22, 2021

Time Event (+)
10:00 - 11:00 Review (Henri Mineur amphitheater / Plenary online session) (+)  
10:00 - 11:00 › Review on Exoplanets and Machine Learning - Ingo Waldmann, University College London - London's Global University  
11:00 - 11:20 Interaction session (Breakout rooms)  
11:20 - 12:20 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
11:20 - 11:35 › An hybrid physics-ML framework to model exoplanetary light curves - Mario Morvan, University College London - London's Global University  
11:35 - 11:50 ›  A deep transfer learning approach to photospheric parameters of CARMENES target stars - Antonio Bello-Garcia, University of Oviedo  
11:50 - 12:05 › Multifrequency Point Source detection with Fully-Convolutional Networks: Performance in realistic microwave sky simulations - José Manuel Casas, Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, Department of Physics  
12:05 - 12:20 › Using a series Machine Learning models for the detection of high-redshift Radio Galaxy candidates. - Rodrigo Carvajal, Institute of Astrophysics and Space Sciences - University of Lisbon  
15:00 - 16:00 Review (Henri Mineur amphitheater / Plenary online session) (+)  
15:00 - 16:00 › Review on Machine Learning for Radio-astronomy - Michelle Lochner, University of the Western Cape, South African Radio Astronomy Observatory, African Institute for Mathematical Sciences  
16:00 - 16:20 Coffee break (Breakout rooms)  
16:20 - 17:20 Contributed (Henri Mineur amphitheater / Plenary online session) (+)  
16:20 - 16:35 › hyphy: Mapping Dark Matter to Hydrodynamics with Posterior Inference - Benjamin A Horowitz, Department of Astrophysical Sciences [Princeton]  
16:35 - 16:50 › MINERVA team: Winning the SKA Science Data Challenge 2 with fast dedicated CNN architectures - David Cornu, Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique  
16:50 - 17:05 › Inference and probabilistic modelling with machine learning for LISA data analysis - Natalia Korsakova, Systèmes de Référence Temps Espace  
17:05 - 17:20 › Deep Neural Networks for detection of sources in radio astronomical maps: main challenges and current achievements. - Renato Sortino, INAF - Osservatorio Astrofisico di Catania - Daniel Magro, University of Malta [Malta]  
17:00 - 18:00 Interaction session (Breakout rooms)  
18:00 - 18:10 Conference summary (Henri Mineur amphitheater / Plenary online session) - Brice Ménard (JHU)  
18:10 - 19:40 Debate (Henri Mineur amphitheater / Plenary online session)  
19:40 - 19:50 Concluding remarks (Henri Mineur amphitheater / Plenary online session)