The programme is available here.

The abstract booklet is available here

Invited Talks

Laura Leal-Taixé: An introduction to Deep Learning for Computer Vision
Emille Ishida: Active Learning in Astronomy
Alberto Krone-Martins: Unsupervised Learning
Giuseppe Longo: Artificial intelligence in Astronomy. Machine learning successes and problems
Mi Dai: The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
Dalya Baron: Mining for novel information in large and complex datasets
John Skilling: Computation in Big Spaces
Zdenka Kuncic: Emergent intelligence from neuromorphic complexity and synthetic synapses in nanowire networks
Rafael S. de Souza: Cosmostatistics Initiative
Jens Jasche: Large Scale Bayesian Analyses of Cosmological Datasets

Contributed Talks

Aisha Alowais: Meteorite Hunting using Deep Learning
Mario Pasquato: Image in science out: a proof of concept with deep learning on molecular cloud simulations
David Cornu: Deep learning for the selection of YSO candidates from IR surveys
Siddhant Agarwal: Unraveling interior evolution of terrestrial planets using Machine Learning
Olivera Latkovic: Recognition of total eclipses in binaries with computer vision
Elodie Choquet: Data Mining in Hubble's archive to find extrasolar systems
Anais Möller: SuperNNova: Bayesian Neural Network light-curve classification
Alexander Chaushev: Classifying Exoplanet Candidates with CNNs: Applications to the NGTS
Adam Malyali: Automated Classification of eROSITA's Transient and Variable Sources
JP Marais: Machine learning techniques to classify transients using LSST: a proof of concept using MeerLICHT
Alison Wong: Modern Neural Networks: A Pathway to Better Adaptive Optics
Ivan Zelinka: Bioinspired Computation in Astrophysics
Markus Bonse: Machine learning based atmosphere prediction for extreme adaptive optics
Maxime Paillassa: MaxiMask: A new tool to identify contaminants in astronomical images using convolutional neural networks
Laura Cabayol Garcia: C Background prediction on astronomical images with deep learning
Michele Delli Veneri: Stellar Formation Rates for photometric samples of galaxies using machine learning methods
Coryn Bailer-Jones: Quasar and galaxy classification in Gaia DR 2
Sebastian Ratzenböck: Searching for what no one is looking for
Miguel Vioque: New catalogue of Pre-Main Sequence objects using AI
Henry Leung: Mapping the Milky Way Galaxy with Deep Learning
F. Korhan Yelkenci: Comparing Performance of Machine Learning Algorithms for Galaxy Classification
Marco Landoni: Machine Learning as a Service - Application of Google Cloud Platform to Machine Learning problems
Torsten Enßlin: Information field theory
Matthew Alger: Extracting Meaningful Features from Early-Science Radio Data
Tobias Schmidt: Deriving Constraints on Quasar Lifetime and Obscuration Using Likelihood-Free Inference
Dominic Bernreuther: Detecting and characterizing interstellar structures with Machine Learning methods
Tilman Troester: Painting with baryons: augmenting N-body simulations with gas using deep generative models
Sebastian Turner: Synergies between low- and intermediate-redshift galaxy population classifications revealed with unsupervised machine learning


Patrick van der Smagt:
     Machine Learning: an introduction in Python notebooks
Fabio Baruffa and Luigi Iapichino:
     Tools and systems for HPC and AI at LRZ
     Running you HPC and AI projects on LRZ systems
     Deep Learning at Scale using Distributed Frameworks
     Performance and Scalability with Optimised Machine Learning Libraries
     Hands-on on Classical Machine Learning
Torsten Enßlin:
      Numerical Information Field Theory - turning data into images the Bayesian way


Philipp Baumeister: Using Mixture Density Networks to infer the interior structure of exoplanets
Giovanni Bruno: Gaussian processes in exoplanet observations
Umar Burhanudin: Machine learning for transient detection and classification with GOTO
Alex Chalevin: ESO Automatization of quality assurance process for ALMA data using supervised learning algorithms
Yen Chen Chen: Classifying Seyfert galaxies with deep learning
Nicholas Chornay: Planetary Nebulae in the Era of Large Surveys
Guido Cupani: Astrocook: a thousand recipes to cook a spectrum
Martin Eriksen: The PAU survey: Photometric redshifts with deep learning
Timothy Gebhard: Learning Causal Pixel-Wise Noise Models to Search for Exoplanets in Direct Imaging Data
Colin Jacobs: Using Deep Learning in the cloud to find Strong Lenses
Ge Liang: Thought about artificial intelligence application for astronomical observation
Alvaro Menduina: Machine Learning for the calibration of non-common path aberrations in ELT-HARMONI
Francisco Miguel Montenegro-Montes: Experimenting with AI in Science Operations
Filip Morawski: Deep learning classification of the continuous gravitational-wave signal candidates
Rakesh Nath: Discriminating between stellar and planetary spectrum using machine learning
Luigi Pulone: Self Organizing Maps In GAIA Deblending Validation
Samira Rezaei: Source Detection of Faint Noisy Radio Surveys
Teymoor Saifollahi: Photometric identification of the Ultra compact dwarf galaxies
Malgorzata Siudek: The complexity of galaxy populations revealed with unsupervised clustering
Hossen Teimoorinia: Applying machine learning methods in astronomy and data centres
Simon Verley: Star Formation at Giant Molecular Cloud Scales in the Elliptical Galaxy NGC 5128 (Centaurus A)
Keming Zhang: deepCR: Cosmic Ray Identification and Image Inpainting with Deep Learning
Shuo Zhang: Determining stellar properties of cool subdwarfs via data-driven method