Beneficiary Partners
| Institution | Main Contact | Team members |
|---|---|---|
| Instituto de Astrofísica de Canarias (Coordinator) | Prof. Johan H. Knapen | Dahimar M Sánchez (Project Manager) |
| Universiteit Gent | Prof. Sven De Rijcke | |
| RijksUniversiteit Groningen | Prof. Kerstin Bunte | |
| Universita degli studi di Napoli Federico II | Prof. Giuseppe Longo | |
| Istituto Nazionale di Astrofisica | Dr Sara Lucatello | |
| Universitat de Barcelona | Prof. Francesca Figueras | |
| Heidelberg Institute for Theoretical Studies | Dr Kai Polsterer | |
| Université Gustave Eiffel | Assoc. Prof. Benjamin Perret |
Associated Partners
| Institution | Main Contact |
|---|---|
| University of Birmingham | Prof. Peter Tino |
| Dirac Institute | Prof. Mario Juric |
| Italian Aerospace Research Centre | Dr Gaetano Zazzaro |
| Courant Institute, New York University | Prof Yann LeCun |
| AVS Added Value Industrial Engineering Solutions SLU | Dr Sergio Salata |
| Adtac BV/Tilt | Dr Tycho Tax |
| Spheer AI BV | Dr Ir Jakko de Jong |
| Vicomtech | Dr Marco Quartulli |
| Pervasive Technologies | Dr Rodolfo Lomascolo |
| ADCIS | Dr François Potevin |
Selected Doctoral Candidates
| Host Institution | PhD Enrollment | Name |
|---|---|---|
| Instituto de Astrofísica de Canarias | Universidad de la Laguna | Minh Ngoc Le |
| Université Gustave Eiffel | Université Gustave Eiffel | Andrea Persici |
| Instituto de Astrofísica de Canarias | Universidad de la Laguna | Marina Dunn |
| RijksUniversiteit Groningen | RijksUniversiteit Groningen | Simone Vilardi |
| Istituto Nazionale di Astrofisica | Universita Degli Studi Di Padova | Milan Quandt Rodriguez |
| Universitat de Barcelona | Universitat de Barcelona | Subhadeep Sarkar |
| Universiteit Gent | Universiteit Gent | Eric Muires |
| RijksUniversiteit Groningen | RijksUniversiteit Groningen | Marcos Antonio Canossa |
| Universita degli studi di Napoli Federico II | Universita degli studi di Napoli Federico II | Cecilia Arrizabalaga Díaz Caneja |
| Hits GGMBH | RijksUniversiteit Groningen | Catarina Corte-Real |
| University of Birmingham | University of Birmingham | Dario Barone |
Biography of Doctoral Candidates
DC01: A fresh view of the missing satellites problem
My name is Minh Ngoc Le (you can call me "Ngoc").
I am interested in studying low surface brightness galaxies through deep imaging surveys to gain insights into galaxy formation and evolution. Fun fact: As a dog person, I enjoy petting dogs whenever I encounter bugs in my code. More about me:
https://le-mn.github.io/
DC02: Semantic Analysis of Deep-Sky Images using Machine Learning and Structural Approaches
Andrea Persici is a Marie Skłodowska-Curie Early-Stage Researcher at the École Supérieure d’Ingénieurs en Électrotechnique et Électronique in Paris, France.
He will be a visiting researcher at New York University (NYU) working with Prof. Yann LeCun, and at the Instituto de Astrofísica de Canarias (IAC) with Prof. Johan H. Knapen.
Andrea received his B.Sc. in Applied Computer Science from Università degli Studi di Urbino Carlo Bo and his M.Sc. in Computer Science and Engineering from Politecnico di Milano.
His interdisciplinary journey, bridging computer science and space science, began early in his academic career. Since then, he has contributed to several ESA missions, including Gaia, Solar Orbiter, and Euclid. Prior to starting his PhD, Andrea was also a research trainee at the European Space Agency (ESA) in Madrid, Spain.
His research interests include artificial intelligence, machine learning, image analysis, big data, and parallel distributed computing.
DC03: Deep Learning for galaxy structure and morphology from massive datasets
Marina M. Dunn is a PhD candidate in Astrophysics at the Instituto de Astrofísica de Canarias (IAC) and the University of La Laguna in the Canary Islands, Spain. Her research focuses on leveraging machine learning to study galaxy formation and evolution through deep imaging surveys, including data from Euclid and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).
Originally from Nashville, Tennessee, USA, Marina earned her B.S. in Astronomy from the University of Arizona in 2018 and an M.S. in Engineering: Data Science from the University of California, Riverside in 2023. Prior to her PhD, she worked research and data science projects at the University of Arizona, Apple, NASA Goddard Space Flight Center, and NASA Langley Research Center, and Lawrence Livermore National Laboratory.
Beyond research, she enjoys watching Formula 1 racing and ice hockey, creating visual art, cycling, gaming, hiking, science outreach, traveling, and spending time with her family.
Marina's personal web site: personal website is https://marinadunn.github.io/
DC04: Detection of structural features in complex, heterogeneous data sets and simulations
Simone Vilardi is a Ph.D. student at the Faculty of Science and Engineering of the University of Groningen in the Netherlands. He received his B.Sc. in Physics and his M.Sc. in Astrophysics from the Università degli Studi di Napoli Federico II, Italy. His research interests include machine learning, artificial intelligence, galaxies and cosmology.
DC05: From the meta-Galaxy to the Milky Way halo
He obtained his Bachelor's degree in Physics from the Universitat de Barcelona (UB) in 2018 and his Master's degree in Astrophysics from the Universidad de La Laguna (ULL) in 2023. He also holds a Master's degree in Physics Education from the Universidad Pública de Navarra (UPNA), earned in 2020.
Originally from Iruña-Pamplona, Basque Country, Milan has a deep passion for science outreach and education, trail running, and post-hardcore music.
DC06: Dynamical influence of dwarf galaxies and internal perturbations on the chemodynamics of the Milky Way disk
Subhadeep Sarkar. I am a Ph.D. candidate in the Gaia research group at the Institut de Ciències del Cosmos, Universitat de Barcelona (ICCUB). My research explores disk dynamics in the Milky Way, focusing on detecting and characterizing dynamical footprints left by non-axisymmetries and past perturbations experienced by our galaxy.
I completed my MPhys degree in Astrophysics at the University of Edinburgh in 2024, where I studied planet formation using SPH simulations. I am interested in developing modular and intuitive software for scientific computing and data visualization in astrophysics.
I am from Kolkata, India. As an avid Rubik's cube enthusiast and semi-avid chess & scrabble player, I always enjoy a fun puzzle!
DC07: Numerical simulations of Milky Way analogues
Eric Muires
Born and bred in the heart of Scandinavia, Eric Muires went on to The UK to receive a Bachelor's of Science in Physics from the University Of Manchester and would subsequently move to Germany to obtain his Master's of Astrophysics from Ludwig-Maximillians University Munich. He currently resides in Gent, Belgium and is working on hydrodynamical simulations for his PhD, with the goal of providing high-resolution moving-mesh simulations of galaxy formation and evolution. The main tools for this are the yet unreleased ShadowSWIFT hydrodynamical code, part of the SWIFT suite for astrophysicists.
Research experience varies from BioPhysics (UoM), Radio Astronomy Instrumentation (ASTRON), Protoplanetary Disks (LMU), and Galaxy Formation and Evolution (UGent).
DC08 Connecting spatial and spectral information of galaxies
Marco A. Canossa Gosteinski is a PhD candidate in Astrophysics at the Kapteyn Astronomical Institute, University of Groningen in the Netherlands. His research focuses on combining spatial and spectral data, currently working with observational data to study merger streams and void galaxies from the CAVITY Survey, as well as merger remnants in Euclid data. He also has a strong interest in understanding the properties of dwarf galaxies.
Originally from Brazil, Marco earned his degree in Physics with an emphasis on Astrophysics at the Federal University of Rio Grande do Sul, where he studied Low Surface Brightness Dwarf Galaxies and their globular cluster systems around NGC 3115.
With over five years of experience in Extragalactic Astronomy, Marco has developed expertise in observational and photometric techniques, as well as cosmological simulation analysis.
You can find more about his work on his personal website:
marcocanossa.wordpress.com
DC09 Machine learning methods for characterisation and validation of spatial structures in astronomical datacubes
Cecilia Arrizabalaga Díaz Caneja
Orginally from Donostia - San Sebastián, Basque Country. Cecilia is a PhD candidate in Astrophysics at the Università degli Studi di Napoli Federico II in Naples, Italy. Her research focuses on the detection of three-dimensional structures in datacubes, currently using ALMA data. Cecilia completed her undergraduate degree in Physics and later pursued a master's degree in Astrophysics at the University of La Laguna.
DC10: Physical analysis of galaxies via spectral reconstruction of deep imaging
Catarina Corte-Real is from Braga, Portugal. She studied Engineering Physics for both her B.Sc. and her M.Sc., in Instituto Superior Técnico, University of Lisbon. She is now at the Heidelberg Institute for Theoretical Studies, doing her PhD in spectral reconstruction of deep imaging.
DC11: Unsupervised clustering in a physically-informed Riemannian geometry.
Dario Barone is a PhD candidate in Computer Science at the University of Birmigham. His main current project aims to develop a clustering technique that leverages Riemannian geometry to better detect structures in the Milky Way Halo. His research interests vary from Probabilistic Modelling and Machine Learning, to Astrophysics and Programming, but also include Stochastic Processes and Complex Networks.
He was born in Milan where he also earned his B.Sc. in Physics at the Università degli Studi di Milano. He earned his M.Sc. in Physics of Data at the Università degli Studi di Padova, after completing his Thesis at the Max Planck Institute for Dynamics and Self-Organization (Göttingen) on the dynamics of disease spreading. He had a short-termed research fellowship position still at Università degli Studi di Padova to work on the interplay between spatial diffusion and mutation of COVID19 analogues.
