- George D. Magoulas
- London, United Kingdom
- I am Professor of Computer Science and Director of Teaching Quality in Birkbeck’s Department of Computer Science and Information Systems, and Co-Director of the Birkbeck Knowledge Lab. I am Fellow of the Advance HE, and Member of the EPSRC College, UK. I hold BEng/MEng and Dr. Eng (University of Patras, Greece) and PGCE(HE) (Brunel University, UK). Before becoming an academic, I held R&D positions in the cement and automotive industries designing fuzzy and neural components for embedded systems. My team designs and develops innovative learning algorithms and system components that employ machine learning and deep networks, sometimes combined with symbolic AI, for psychophysiological data modelling and classification, and intelligent learning environments. The aim is to enable systems to exhibit different levels of intelligence, learn from data and transfer knowledge to new contexts. Our work has received best paper awards by the IEEE (2000 and 2008), the European Network on Intelligent Technologies for Smart Adaptive Systems (2001 and 2004), the International Association for Development of the Information Society (2006), the ACM (2009) and KES International (2010).
11 Sept 2019
New articles
Evolving Connectionist Models to Capture Population Variability Across Language Development: Modelling Children’s Past Tense Formation, Artificial Life.
Challenging the Alignment of Learning Design Tools with HE Lecturers’ Learning Design Practice, in the Proceedings of the European Conference on Technology Enhanced Learning (EC-TEL 2019)
Transformative Power of Smart Technologies Enabled by Advances in AI: Changing Landscape for Digital Marketing, in the Handbook of Research on Innovations in Technology and Marketing for the Connected Consumer
30 Jan 2019
New papers
Neural Adaptive Admission Control Framework: SLA-Driven Action Termination for Real-Time Application Service Management, in Enterprise Information Systems
Customised Ensemble Methodologies for Deep Learning: Boosted Residual Networks and Related Approaches, in Neural Computing and Applications.
User Model Interoperability in Education: Sharing Learner Data Using the Experience API and Distributed Ledger Technology, in Responsible Analytics and Data Mining in Education, Badrul H. Khan, Joseph Rene Corbeil, Maria Elena Corbeil (eds), Routledge.
20 Dec 2017
New papers
Towards an Educational Design Pattern Language for Massive Open Online Courses (MOOCs), in Proceedings of the 24th Conference on Pattern Languages of Programs (PLoP 2017)
The cloudUPDRS app: a medical device for the clinical assessment of Parkinson's Disease, in Pervasive and Mobile Computing
Hardening against adversarial examples with the Smooth Gradient Method, in Soft Computing
Developing and Educational Design Pattern Language for MOOCs
at the Anais do XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017)
9 Jun 2017
New papers
Boosted Residual Networks at the 18th EANN 2017
Distillation of Deep Learning Ensembles as Regularisation method in Intelligent Systems Reference Library, Advances in Hybridization of Intelligent Methods, Springer.
Interval Methods for Resolving Neural Computation Issues at SWIM-SMART 2017
Users Perceptions of E-learning Environments and Services Effectiveness: The Emergence of the Concept Functionality Model, Journal of Enterprise Information Management
Subscribe to:
Posts (Atom)