This course consists of 2 introductory modules: (1) Techniques of AI (introductory tour of AI techniques, ranging from symbolic techniques for problem-solving based on logic, through theorem proving and statistical methods, to basic machine learning in two different forms. Search techniques are covered. The course takes an agent-based perspective on AI) and (2) Machine Learning (A more specialist course introducing the theory and practice of machine learning in general, covering a broad range of techniques, with a particular focus on reinforcement learning).
VUB: Geraint A. Wiggins (Geraint.Wiggins@vub.be)
Geraint A. Wiggins studied Mathematics and Computer Science at Corpus Christi College, Cambridge and holds 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 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.
UPF: Vicenç Gómez (email@example.com)
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 center (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.
UPF: Javier Segovia (firstname.lastname@example.org)
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 i Informàtica Industrial (IRI, CSIC-UPC). In 2019, his PhD thesis at UPF was awarded with 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.
UOG: Mari Paananen (email@example.com)
Mari Paananen joined the School of Business, Economics and Law at the University of Gothenburg in October 2017. She was formerly an Associated Professor of Accounting at the University of Exeter, at the department of accounting. Mari graduated with a PhD from the University of North Texas and up until present, she worked primarily in the United Kingdom. Mari’s current research involves examining the capital market effect of financial reporting disclosures using computerized text analysis, whether Internet data can predict financial distress, the economic impact/consequences of social networks and the magnitude and reasons for balance sheet management among small and medium sized (SME) companies. https://www.gu.se/en/about/find-staff/maripaananen
UOG: Miroslaw Staron (firstname.lastname@example.org)
UOL: Zoran Bosnić (email@example.com)
Zoran Bosnić works as a full professor at Artificial Intelligence Department in the Laboratory for Cognitive Modelling. He gives lectures on the following courses: Computer Networks, Functional Programming, and also lectures several classes at 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.
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, and 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 Ph.D. in Sociology from the University of Chicago and a Sc.B. in Computer Science from Brown University.
CY: Laura HERNANDEZ (firstname.lastname@example.org)
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 or 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-pollinators networks), and also in 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 on the studied system. Since 2016 I am the director of the OpLaDynproject http://project.u-cergy.fr/~opladyn/, where an international team composed by researchers of different disciplines (Physics, Computer Science, Linguistics, Law, Philosophy, Communication) explore show to use massive data bases to understand problems in social sciences. I am also deeply interested in training the young generations to this interdisciplinary approach of research and I usually work in an international context both in research and teaching activities. I have introduced in 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 to young researchers of different disciplines.
CY: Dimitrios KOTZINOS (email@example.com)
Dimitris Kotzinos is a Professor at the Department of Computer Science of the CY Cergy Paris University, member of the ETIS Lab and member of the MIDI team of the lab. His main research interests include data management algorithms, techniques and tools; 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 in data engineering and analysis problems.