Instructors
Daria Dobrycheva
Daria Dobrycheva is a senior researcher at the Department of Extragalactic Astronomy and Astroinformatics of the Main Astronomical Observatory of the National Academy of Sciences of Ukraine. Her main research interest concerns the large-scale structure of the Universe, in particular the spatial distribution and physical properties of galaxies, the morphological classification of galaxies according to various features of their structure, and evolution on cosmological scales. The main focus of her work is on applying machine learning techniques in astronomical data analysis. Her team has developed automated approaches for the morphological classification of galaxies and the identification of their intrinsic features. She is now taking part in a project for searching Milky Way galaxy analogues using machine learning methods. She is also involved in the search for exocomet transits using machine learning.
Guillaume Thomas
Guillaume Thomas is a postdoctoral researcher at the Instituto de Astrofísica de Canarias (IAC), where he has been since 2020. He earned his Ph.D. in 2017 from the Strasbourg Observatory and spent three years at the NRC Herzberg Astronomy and Astrophysics in Victoria, BC as a National Research Council fellow. His research focuses on Galactic archaeology and Galactic dynamics, with a particular focus on the outer components of the Milky Way, the stellar halo, and in the objects that inhabit it.
Hugues Talbot
Hugues Talbot is a distinguished professor of applied mathematics at Centre for Visual Computing, CentraleSupelec, University Paris-Saclay. His research interest include mathematical morphology, discrete and continuous optimisation with applications to medical imaging, materials science and other domains, such as astronomy :-). Further information at https://hugues-talbot.github.io
Johan Knapen
Johan Knapen is a research professor at the IAC in Tenerife, interested in the structure and evolution of nearby galaxies using a variety of observational and analysis techniques. On the observational side, deep imaging with Euclid and the VRO LSST as well as IFU spectroscopy with WEAVE are important in his current research, and on the analysis side the use of AI techniques and the treatment of instrumental effects to reach the deepest feasible levels of surface brightness. He is the Principal Investigator and Coordinator of the EDUCADO Doctoral Network, as well as of the EU-funded ExGal-Twin Twinning/Widening project at the IAC. Side interests are the world-wide development of professional astronomy, and training people on how to write more effectively.
Kerstin Bunte
Kerstin Bunte is a professor of machine learning for interdisciplinary data science at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands.
In 2015 she got a European Marie Sklodowska-Curie Fellowship (Project ID: 659104) at the University of Birmingham, was partner in the European ITN: SUNDIAL, Project ID: 721463 and now partner in the European DTN: EDUCADO.
Her recent work has focused on the development of interpretable machine learning techniques for interdisciplinary data analysis and principled inclusion of expert knowledge with applications in medicine, astronomy and smart industry.
Further information can be obtained from http://www.cs.rug.nl/~kbunte/.
Malgorzata Siudek
Malgorzata Siudek is a postdoctoral fellow at the Instituto de Astrofísica de Canarias, specializing in galaxy evolution and large-scale surveys. Her work bridges astrophysics and machine learning, developing AI-driven tools to analyze multi-wavelength data. She co-leads projects within major collaborations like DESI and Euclid, and mentors early-career researchers across Europe. With a strong international profile and hands-on experience in AI applications, she actively drives innovation in next-generation galaxy surveys. She brings her expertise in machine learning and galaxy physics to empower the next generation of researchers.
Michael H. F. Wilkinson
Michael H. F. Wilkinson obtained an MSc in astronomy from the Kapteyn Astronomical Institute, University of Groningen in 1993, after which he worked on image analysis of intestinal bacteria at the Department of Medical Microbiology, University of Groningen, obtaining a PhD at the Institute of Mathematics and Computing Science, also in Groningen, in 1995. He was appointed as researcher at the Centre for High Performance Computing in Groningen working on simulating the intestinal microbial ecosystem on parallel computers. After this he worked as a researcher at the Johann Bernoulli Institute for Mathematics and Computer Science on image analysis of diatoms. He is currently associate professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, working on morphological image analysis and especially connected morphology and models of perceptual grouping. An important research focus is on handling giga- and tera-scale images in remote sensing, astronomy and other digital imaging modalities.
Nikolaos Gianniotis
Nikolaos Gianniotis is a computer scientist with a background in machine learning. I completed my MSc and PhD at the University of Birmingham, UK. During my postdoctoral work, I had the opportunity to collaborate in interdisciplinary settings, working with seismologists, astronomers and industry partners. My research focuses on approximate Bayesian inference, Gaussian processes, and dimensionality reduction. I currently work as a staff scientist at the Heidelberg Institute for Theoretical Studies (HITS), where I develop machine learning methods for applications in astronomy.
Peter Tino
Peter Tino holds a Chair position in Complex and Adaptive Systems at the School of Computer Science, University of Birmingham, UK. His interests span machine learning, neural computation, probabilistic modelling and dynamical systems. Peter is fascinated by the possibilities of cross-disciplinary blending of machine learning, mathematical modelling and domain knowledge in a variety of scientific disciplines ranging from astrophysics to bio-medical sciences. He has served on editorial boards of a variety of journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, Scientific Reports, and Neural Computation and (co-)chaired Task Force on Mining Complex Astronomical Data and Neural Networks Technical Committee (IEEE Computational Intelligence Society). He also co-leads the Alan Turing Institute Space Science Interest Group. Peter led an EPSRC-funded consortium of six UK universities on developing new mathematics for personalised healthcare. He was a recipient of the Fulbright Fellowship to work at NEC Research Institute, Princeton, USA, on dynamics of recurrent neural networks, UK–Hong-Kong Fellowship for Excellence, three Outstanding Paper of the Year Awards from the IEEE Transactions on Neural Networks and the IEEE Transactions on Evolutionary Computation.
Petra Awad
Petra Awad is a postdoctoral scientist and developer for the Euclid telescope Near-Infrared (NIR) pipeline at the University of Leiden in the Netherlands. As part of the Euclid science ground segment, her main objective is to improve the processing of NIR images resulting in better quality data for the upcoming releases. In addition to her work within the Euclid collaboration, her main research interests constitute the tracing of cosmic filaments within the large-scale structure in both simulations and observational surveys, as well as studying the properties of galaxies as a function of their cosmic environments. Other interests include the characterization of the morphology and history of stellar streams within the Milky Way halo which are remnants of past interactions with our Galaxy. These explorations have been conducted with Machine Learning tools designed by our team and serve the detection and analysis of filamentary structure within astrophysical settings.