- Lead: Geraint A. Wiggins (VUB). Email: Geraint.Wiggins@vub.be
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Geraint A. Wiggins studied Mathematics and Computer Science at Corpus Christi College, Cambridge and holds a PhDs from the University of Edinburgh’s Edinburgh’s Artificial Intelligence and Music Departments. His main research area is computational creativity, which he views as an intersection of artificial intelligence and cognitive science. He is interested in understanding how humans can be creative by building computational models of mental behaviour and comparing them with the behaviour of humans. He has worked at the University of Edinburgh and three colleges of the University of London: City (where he served as Head of Computing, and Senior Academic Advisor on quality), Goldsmiths, and Queen Mary (where he served as Head of School of Electronic Engineering and Computer Science). In 2018, he moved his Computational Creativity Lab to the Vrije Universiteit Brussel, in Belgium. He is a former chair of SSAISB, the UK learned society for AI and Cognitive Science, and of the International Association for Computational Creativity, of whose new journal he is editor-in-chief. He is an associated editor (English) of the Musicae Scientiae (the journal of the European Society for the Cognitive Sciences of Music), a consulting editor of Music Perception (the journal of the Society for Music Perception) and an editorial board member of the Journal of New Music Research.
- Partner: Lynn Houthuys (VUB). Email: Lynn.Houthuys@vub.be
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Lynn Houthuys (°1990) obtained master degrees in Computer Sciences and Artificial Intelligence, both from KU Leuven. In 2018 she obtained a PhD degree in Electrical Engineering at KU Leuven for her work regarding multi-view learning. From 2019 until 2023, she worked as a researcher and lector at Thomas More Applied University where she carried out applied machine learning research projects in close collaboration with Belgian SMEs. She is currently a tenure track professor at the AI Lab of the Vrije Universiteit Brussel. Her research interests encompass Machine Learning, and in particular how to fuse information in a multi-modal setting with a focus on kernel-based and neural network architectures.
- Partner: Vicenç Gómez (UPF). Email: vicen.gomez@upf.edu
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Vicenç Gómez is currently a tenure-track professor in the Artificial Intelligence and Machine Learning Research Group at the Universitat Pompeu Fabra (UPF), where he also coordinates the MSc. in Intelligent & Interactive Systems. Before that, he was a post-doctoral researcher at the Donders Institute for Brain, Cognition and Behavior (2011–2014) and at the Radboud university medical centre (2009–2011) in Nijmegen (The Netherlands). In 2014, he was awarded a transnational academic career grant (FP7 Marie Curie Actions) and later in 2016, he obtained a Ramon y Cajal fellowship. He has held visiting appointments in Los Alamos National Laboratory (USA), the IAS group at Technische Universitaet Darmstadt (Germany), and at University College London (UK). His main research interests are artificial intelligence and machine learning. In particular, developing probabilistic inference and reinforcement learning methods and their use in a diverse set of application domains.
- Partner: Javier Segovia (UPF). Email: javier.segovia@upf.edu
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Javier Segovia-Aguas is a post-doctoral researcher in the RLeap project in the Artificial Intelligence and Machine Learning Research Group at the Universitat Pompeu Fabra (UPF), where he also teaches Artificial Intelligence (AI). He was previously a post-doctoral researcher in the H2020 IMAGINE project in the Perception and Manipulation group (2018-2020) at the Institut de Robòtica Informàtica Industrial (IRI, CSIC-UPC). In 2019, his PhD thesis at UPF was awarded the best European dissertation award in AI, sponsored by EurAI, and in 2020 he obtained a Juan de la Cierva – Formación fellowship. In 2018, he held a visiting appointment at The University of Melbourne (AUS). His main research interests are in automated planning and machine learning, where solutions in the form of general programs or policies can be learned to solve complex planning problems.
- Partner: Miroslaw Staron (GU). Email: miroslaw.staron@cse.gu.se
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http://www.staron.nu
- Partner: Zoran Bosnić (UL). Email: zoran.bosnic@fri.uni-lj.si
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Zoran Bosnić works as a full professor in the Department of Artificial Intelligence at the Laboratory for Cognitive Modelling. He gives lectures on the following courses: Computer Networks, Functional Programming, and also lectures several classes for the doctoral studies. His research work combines advanced statistical methods with useful application areas such as mining from data streams, recommender systems, user behaviour profiling, e-learning systems and computer communications.
- Partner: Jure Zabkar (UL). Email: jure.zabkar@fri.uni-lj.si
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Dr. Jure Zabkar is an assistant professor and researcher at the Artificial Intelligence Laboratory at the University of Ljubljana, Faculty of Computer and Information Science. He received a BSc degree in Mathematics in 1999 and BSc in Computer Science in 2004, both from the University of Ljubljana. He conducts research in machine learning and data mining, qualitative reasoning, cognitive robotics and systems for decision support with applications in healthcare.
- Partner: Michael Castelle (UW). Email: M.Castelle.1@warwick.ac.uk
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Michael Castelle is an Assistant Professor in the Centre for Interdisciplinary Methodologies at the University of Warwick. His research is at the intersection of the economic sociology of markets and platforms, the history of late 20th-century computing, and science and technology studies. He is currently co-investigator on the 3-year ESRC-funded ‘Shaping 21st Century AI’ project in collaboration with scholars in Canada, France, and Germany, studying controversies in contemporary artificial intelligence research. He has a PhD in Sociology from the University of Chicago and an Sc.B. in Computer Science from Brown University.
- Partner: Laura HERNANDEZ (CY). Email: laura.hernandez@cyu.fr
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I’m a physicist, and since 1993, a tenured Associate Professor at Laboratoire de Physique Théorique et Modélisation (LPTM), a laboratory jointly run by the CNRS and CY Cergy Paris University, in France. My research work focuses on the study of Complex Systems not only in Physics but also in other fields like Ecology, Economy and Social Sciences. I adopt the point of view of Physics to explore non-physical systems, mainly using the tools and the concepts of Statistical Physics, Dynamical Systems, and Network Theory. For instance, I work on a complex network approach of Mutualistic Ecosystems, (like plant-pollinator networks), and also on problems of Cultural and Opinion Dynamics. I’m interested in studying these systems both from a data-based approach and also by theoretical models based on simple rules issued from the disciplinary knowledge of the studied system. Since 2016 I am the director of the OpLaDynproject http://project.u-cergy.fr/~opladyn/, where an international team composed of researchers of different disciplines (Physics, Computer Science, Linguistics, Law, Philosophy, Communication) explore show to use of massive data bases to understand problems in social sciences. I am also deeply interested in training the young generations in this interdisciplinary approach to research and I usually work in an international context both in research and teaching activities. I have introduced 2009 a Complex System Path to the Master of Theoretical Physics and Applications which I had directed until 2015. I’m also in charge of a course on Network Theory for the Doctoral School of CYU, adapted for young researchers of different disciplines.
- Partner: Dimitrios KOTZINOS (CY). Email: dimitrios.kotzinos@cyu.fr
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Dimitris Kotzinos is a Professor at the Department of Computer Science of the CY Cergy Paris University, a member of the ETIS Lab and a member of the MIDI team of the lab. His main research interests include data management algorithms, techniques and tools; the development of methodologies, algorithms and tools for web-based information systems, portals and web services; and the understanding of the meaning (semantics) of interoperable data and services on the web. Recently he has started working on studying the formation and evolution of discussions in online social networks using Machine Learning (ML) and Artificial Intelligence (AI) techniques. Additionally, he has started working in the area of accountability, explainability and fairness of the ML and AI algorithms, especially when applied to data engineering and analysis problems.
- Partner: Jana Faganeli Pucer (UL). Email: jana.faganeli@fri.uni-lj.si
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Jana Faganeli Pucer is an assistant professor and researcher at the University of Ljubljana, Faculty of Computer and Information Science. She received
her BSc degree in Electrical engineering in 2007 and her PhD in Computer Science in 2014, both from the University of Ljubljana. Her research is focused on machine learning, especially machine learning methods in meteorology and environmental sciences.
