PROGRAMME
Times are in CEST (UTC +2)
Invited Talks (IS): 30+10 min
Contributed Talks: 15 + 5 min
Posters: 3 + 2 min
The abstract booklet is available here.
| Monday 16 May | |
|---|---|
| Chairperson: | Michael Sterzik |
| 13:45 - 14:00 | Conference Welcome |
| 14:00 - 14:40 | Pierre-Philippe Mathieu (IS): The Rise of Artificial Intelligence for Earth Observation (AI4EO) |
| 14:40 - 15:00 | Marcos López-Caniego: ESA Virtual Assistant in ESASky: enabling archival data exploration via natural language processing |
| 15:00 - 15:15 | BREAK |
| 15:15 - 15:55 | Emille Ishida (IS): Fink: incorporating expert knowledge in machine learning applications for large scale sky surveys |
| 15:55 - 16:15 | Jeroen Audenaert: An all-sky stellar variability machine learning classification framework for TESS and PLATO |
| 16:15 - 16:30 | BREAK |
| 16:30 - 16:50 | John F. Suárez Pérez: Assessing the quality of massive spectroscopic surveys with unsupervised machine learning |
| Tuesday 17 May | |
|---|---|
| Chairperson: | Paula Sánchez Sáez |
| 09:00 - 09:20 | Michele Delli Veneri: Data Cleaning and Detection and Characterization of Sources in ALMA data through Deep Learning |
| 09:20 - 09:40 | Juan Gil: Log Analysis as an Operational Tool at Paranal Observatory |
| 09:40 - 10:00 | Michael Johnson: VAMPIRA - Automated Provenance Generation for Astronomical Pipelines |
| 10:00 - 10:15 | BREAK |
| 10:15 - 10:35 | Stefan Schuldt: Machine learning investigations for LSST: Strong lens mass modeling and photo-z estimation |
| 10:35 - 11:15 | Elena Cuoco (IS): Artificial Intelligence application to Gravitational wave transient signals |
| 11:15 - 11:30 | BREAK |
| 11:30 - 11:50 | Caroline Heneka: Networks Learning the Universe: From 3D (hydrogen tomography) to 1D (classification of spectra) |
| 11:50 - 12:10 | Sandor Kruk: Using deep learning and crowdsourcing to survey asteroid trails in ESA's Hubble data archive |
| 12:10 - 14:00 | LUNCH |
| Chairperson: | Vicente Navarro |
| 14:00 - 14:40 | Camille Avestruz (IS): AI Enabled Data Exploitation for Astrophysics and Cosmology |
| 14:40 - 15:00 | Daniel Muthukrishna: Real-Time Detection of Anomalies in Large-scale transient surveys |
| 15:00 - 15:15 | BREAK |
| 15:15 - 15:55 | Maggie Lieu (IS): Pushing the limits of Astronomy using AI |
| 15:55 - 16:15 | Carter Rhea: A New Paradigm in X-ray Spectral Analysis |
| 16:15 - 16:30 | BREAK |
| 16:30 - 17:00 | POSTER SESSION 1 |
| Wednesday 18 May | |
|---|---|
| Chairperson: | Michelle Lochner |
| 09:00 - 09:20 | Maxime Quesnel: A physics-based deep learning approach for focal-plane wavefront sensing |
| 09:20 - 09:40 | Bartomeu Pou Mulet: A Non-Linear Control Method with Reinforcement Learning for Adaptive Optics with Pyramid Sensors |
| 09:40 - 10:00 | Jalo Nousiainen: Advances in model-based reinforcement learning for adaptive optics control |
| 10:00 - 10:15 | BREAK |
| 10:15 - 10:55 | Wolfgang Kerzendorf (IS): AI for proposal handling and selection |
| 10:55 - 11:15 | Siouar Bensaid: Segmentation of the Galactic ISM Filaments using Deep Learning and enriched HiGAL catalogue |
| 11:15 - 11:30 | BREAK |
| 11:30 - 12:00 | POSTER SESSION 2 |
| Friday 20 May | |
|---|---|
| Chairperson: | Gaitee Hussain |
| 09:00 - 09:40 | François Lanusse (IS): Going Beyond Common Deep Learning Limitations with Deep Probabilistic Modeling |
| 09:40 - 10:00 | Jason Spyromilio: Why AI is wrong for apparatus calibration |
| 10:00 - 10:15 | BREAK |
| 10:15 - 10:35 | Claudia Comito: The missing link between massive data and AI: parallel computing with Heat |
| 10:35 - 10:55 | Nick Cox: EXPLORE - Innovative Scientific Data Exploration and Exploitation Applications for Space Sciences |
| 10:55 - 11:15 | Srividya Subramanian: Astrophysics with INODE |
| 11:15 - 11:30 | BREAK |
| 11:30 - 12:10 | Michelle Ntampaka (IS): ML for Astronomy: Cautionary Tales for the Community |
| 12:10 - 12:30 | Final comments (M. Sterzik/C. Arviset) |
E-Posters
Session 1:
1. Miguel Doctor Yuste: EVA (ESA Virtual Assistant): The conversational AI platform for Space
2. Ali-Dib Mohamad: Identification of craters and boulders with deep learning
3. Fiorenzo Stoppa: ASID-L. Fast Source Localization in Optical Images
4. Christophe Morisset: Machine learning methods applied to interstellar medium studies
5. Connor Bottrell: Unraveling galaxy merger histories with deep learning
6. Francesco Guarneri: QUBRICS: machine learning for searching bright, high-redshift quasars
Session 2:
7. Cristiano Sabiu: Machine Learning the nature of Dark Matter with 21cm tomography
8. Roland Szakacs: Detecting MgII Absorbers in Quasar Spectra: A Machine Learning Approach
9. Magda Arnaboldi: Enhanced discoverability and download of the ESO science data content via programmatic access
10. Maja Jabłońska: Differentiable stellar disk integration
11. Suhyun Shin:The survey for low-luminosity quasars at z~5 using artificial neural network and Bayesian statistics
12. MD Redyan Ahmed: Investigating the efficiency of the SPIIR pipeline in O3 offline Gravitational Wave search