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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).

Referred articles and chapters in edited volumes


  1. Karoudis K. and Magoulas G.D., User Model Interoperability in Education: Sharing Learner Data Using the Experience API and Distributed Ledger Technology, in Badrul Khan, Joseph Rene Corbeil, and Maria Elena Corbeil (eds), Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision-Making, Routledge, Taylor & Francis, forthcoming.
  2. Mosca A. and Magoulas G.D., Distillation of Deep Learning Ensembles as a Regularisation method in Hatzilygeroudis I., Palade, V. (eds.), Advances in Hybridization of Intelligent Methods, Smart Innovation, Systems and Technologies, vol. 85, Extended and revised versions of invited papers presented at the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), held in The Hague, Holland, in August 2016, Springer, 2018.
  3. Mosca A. and Magoulas G.D., Learning Input Features Representations in Deep Learning, Angelov P., Gegov A., Jayne C., Shen Q.(eds.), Advances in Intelligent Systems and Computing, vol. 513, Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK, pp. 433-445, Springer, 2016.
  4. Sikora T.D., Magoulas G. D., Finding Relevant Dimensions in Application Service Management Control. Liming Chen, Supriya Kapoor, Rahul Bhatia (Eds.), Intelligent Systems for Science and Information, Extended and Selected Results from the Science and Information Conference, Studies in Computational Intelligence, vol. 542, pp 335-353, 2014.
  5. Charlton P. & Magoulas G.D. Context-aware Framework for Supporting Personalisation and Adaptation in Creation of Learning Designs. S. Graf, F. Lin, Kinshuk & R. McGreal (Eds.) Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers. Hershey, PA: IGI Global, 2011.
  6. Peng C-C and Magoulas G.D., Nonmonotone Learning of Recurrent Neural Networks in Symbolic Sequence Processing Applications, Palmer-Brown, D., Draganova, Ch., Pimenidis, E., Mouratidis, H. (Eds.), Engineering Applications of Neural Networks, Communications in Computer and Information Science Series, Springer Berlin Heidelberg, vol. 43, pp. 325-335, 2009, ISBN: 978-3-642-03969-0.
  7. Charlton, P. and Magoulas, G. D. Next Generation Environments for Context-Aware Learning Design, Hatzilygeroudis, I. and Prentzas, J. (eds.), Combinations of Intelligent Methods and Applications, vol. 8, Smart Innovation, Systems and Technologies Series, Springer Berlin Heidelberg, pp. 125-143, 2011, ISBN: 978-3-642-19618-8.
  8. Van Labeke N., Magoulas G.D. and Poulovassilis A., Searching for “People Like Me” in a Lifelong Learning System, Learning in the Synergy of Multiple Disciplines, Lecture Notes in Computer Science, Volume 5794, Proceedings of the 4th European Conference on Technology Enhanced Learning (EC-TEL 2009) Nice, France, Sept 29–Oct 2, 2009, U. Cress, V. Dimitrova, M. Specht (Eds.), Springer, pp. 106-111, 2009.
  9. Dimakopoulos D. and Magoulas G.D., An architecture for a personalised mobile environment to facilitate contextual lifelong learning, chapter 12 in H. Ryu and D. Parsons (eds.), Innovative Mobile Learning, 2009.
  10. Peng C.-C. and Magoulas G.D., Sequence Processing with Recurrent Neural Networks, J. R. R. Dopico, J. Dorado, and A. Pazos (eds), Encyclopedia of Artificial Intelligence, Information Science Reference, ISBN:  978-1-59904-849-9, 2008.
  11. Van Labeke N., Poulovassilis A. and Magoulas G.D., Using Similarity Metrics for Matching Lifelong Learners, Intelligent Tutoring Systems, Lecture Notes in Computer Science, vol. 5091, Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS 2008), B. P.Woolf, E. Aïmeur, R. Nkambou, S. Lajoie (Eds.), Springer, pp. 142-151, 2008.
  12. de Freitas S., Harrison I., Magoulas G.D., Papamarkos G., Poulovassilis A., Van Labeke N., Mee A., and Oliver M., L4All: a Web-Service Based System for Lifelong Learners, S. Salerno, M. Gaeta, P. Ritrovato, N. Capuano, F. Orciuoli, S. Miranda and A. Pierri (eds.), The Learning Grid Handbook: Concepts, Technologies and Applications, Volume 2: The Future of Learning, IOS Press, 2008, ISBN: 978-1-58603-829-8.
  13. Magoulas G.D., User Modeling in Information Portals, Encyclopedia of Portal Technologies and Applications, Arthur Tatnall (ed.), vol II, Information Science Reference, ISBN: 978-1-59140-989-2, 2007.
  14. Peng C.-C. and Magoulas G.D., Adaptive Self-scaling Non-monotone BFGS Training Algorithm for Recurrent Neural Networks, Lecture Notes in Computer Science vol. 4668, Artificial Neural Networks Part I – J. Marques de Sá et al. (eds.). Presented as a full paper at the 17th International Conference Artificial Neural Networks, pp. 259–268, 2007. 
  15. Plagianakos V.P., Magoulas G.D. and Vrahatis M.N., Improved learning of neural nets through global search, chapter 15 in Global Optimization - Scientific and Engineering Case Studies, János D. Pintér (ed.), Series: Nonconvex Optimization and Its Applications, vol. 85, Springer-Verlag New York Inc, (ISBN: 0-387-30408-8), pp. 361- 388, 2006. 
  16. Magoulas G.D, Web-based instructional systems. Encyclopaedia of Human Computer Interaction, Claude Ghaoui (ed.), IDEA publishing (ISBN: 1-59140-562-9), pp. 729-738, 2005.
  17. Magoulas G.D. and Vrahatis M.N., Parameter optimization algorithm with improved convergence properties for adaptive learning. Lecture Series on Computer and Computational Sciences, vol. 3, Frontiers of Computational Science, G. Maroulis and Th. Simos (eds.), Brill Academic Publishers, Leiden, The Netherlands (ISBN 90-6764-442-0), pp.384-398, 2005.
  18. Frias-Martinez E., Magoulas G.D., Chen S., and Macredie R. Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia. Lecture Notes in Computer Science, vol. 3137, Adaptive Hypermedia and adaptive web-based systems, Paul De Bra, Wolfgang Nejdl (eds), Springer, pp. 104-113, 2004. Presented as a full paper at 3rd Int Conf Adaptive Hypermedia. 
  19. Magoulas, G. D., Chen, S. Y., and Dimakopoulos, D. A Personalised Interface for Web Directories based on Cognitive Styles. Lecture Notes in Computer Science, vol. 3196, User-Centered Interaction Paradigms for Universal Access in the Information Society: Revised Selected Papers of the 8th ERCIM Workshop on User Interfaces for All, Springer-Verlag, pp. 159-166, 2004. 
  20. Stathacopoulou R., Grigoriadou M., Samarakou M., Magoulas G.D., Using Simulated Students for Machine Learning. Lecture Notes in Computer Science, vol. 3220, James C. Lester, Rosa Maria Vicari, Fabio Paraguau (eds.), Springer, pp. 889-891, 2004. Presented as a short paper at the 7th International Conference on Intelligent Tutoring Systems (ITS 2004).
  21. Anastasiadis A.D., Magoulas G.D., and Liu X. Classification of Protein Localisation Patterns via Supervised Neural Network Learning. Lecture Notes in Computer Science, vol. 2810, Advances in Intelligent Data Analysis V, M. Berthold, H.-J. Lenz, E. Bradley et al. (eds.), Berlin: Springer-Verlag, pp. 430-439, 2003. Presented as a short paper at the 5th International Symposium on Intelligent Data Analysis. 
  22. O’Neill P., Magoulas G. D., and Liu X. Obtaining Quality Microarray Data via Image Reconstruction. Lecture Notes in Computer Science, vol. 2810, Advances in Intelligent Data Analysis V, M. Berthold, H.-J. Lenz, E. Bradley et al. (eds.), Berlin: Springer-Verlag, pp. 364-375, 2003. Presented as a full paper at the 5th International Symposium on Intelligent Data Analysis. 
  23. Stathacopoulou R., Grigoriadou M., Magoulas G. D. and Mitropoulos D., A Neuro-Fuzzy Approach in Student Modeling, Lecture Notes in Computer Science (LNCS) Vol. 2702, Springer-Verlag Heidelberg, pp. 337-341. Presented as poster at 9th International Conference on User Modeling (UM2003). 
  24. Grigoriadou, M., Kornilakis, H., Papanikolaou, K.A., and Magoulas, G.D. Fuzzy Inference for Student Diagnosis in Adaptive Educational Systems. Lecture Notes in Artificial Intelligence, vol. 2308, Methods and Applications of Artificial Intelligence: Vlahavas and C.D. Spyropoulos (eds.), Berlin: Springer-Verlag, pp. 191-202, 2002. Presented as a full paper at the 2nd Hellenic Conference on AI, SETN2002. 
  25. Papanikolaou K.A., Grigoriadou M., Kornilakis H., and Magoulas G.D. INSPIRE: an INtelligent System for Personalized Instruction in a Remote Environment. Lecture Notes in Computer Science, vol. 2266, Hypermedia: Openess, Structural Awareness, and Adaptivity, S. Reich. M. Tzagarakis, P.M.E. De Bra, Berlin (eds.), Heidelberg: Springer-Verlag, pp. 215-225, 2002. 
  26. Magoulas G.D. and Prentza A., Machine learning in medical applications. Lecture Notes in Artificial Intelligence, vol. 2049, Machine Learning and its Applications: Advanced Lectures, G. Paliouras, V. Karkaletsis and C.D. Spyropoulos (Eds.), Springer-Verlag, pp. 300-307, 2001. 
  27. Parsopoulos, K., Plagianakos, V.P., Magoulas, G.D., and Vrahatis M.N., Improving the particle swarm optimizer by function “stretching”. Advances in convex analysis and global optimization vol. 54, Noncovex Optimization and its Applications, Hadjisavvas N. and Pardalos P. (ed.), Kluwer Academic Publishers, Dordrecht, The Netherlands (ISBN 0-7923-6942-4), Chapter 28, pp.445-457, 2001. 
  28. Plagianakos V.P., Magoulas G.D. and Vrahatis M.N., Supervised training using global search methods. Advances in convex analysis and global optimization, vol. 54, Noncovex Optimization and its Applications, Hadjisavvas N. and Pardalos P. (ed.), Kluwer Academic Publishers, Dordrecht, The Netherlands (ISBN 0-7923-6942-4), Chapter 26, pp.421-432, 2001. 
  29. Plagianakos V.P., Magoulas G.D. and Vrahatis M.N., Learning rate adaptation in stochastic gradient descent. Advances in convex analysis and global optimization, vol. 54, Noncovex Optimization and its Applications, Hadjisavvas N. and Pardalos P. (ed.), Kluwer Academic Publishers, Dordrecht, The Netherlands (ISBN 0-7923-6942-4), Chapter 27, pp.433-444, 2001. 
  30. Papanikolaou K., Magoulas G.D., and Grigoriadou M., A connectionist approach for supporting personalized learning in a web-based learning environment. Lecture Notes in Computer Science, vol. 1892, Springer, pp. 189-201, 2000. Presented as a full paper at International Conference on Adaptive Hypermedia and Adaptive Web-based System. 
  31. Magoulas G.D., Plagianakos V.P., Androulakis G.S. and Vrahatis M.N., A framework for the development of globally convergent adaptive learning rate algorithms. Advances in Intelligent Systems and Computer Science, N.E. Mastorakis ed., World Scientific and Engineering Society Press, pp.207-212, 1999. 
  32. Plagianakos V.P., Magoulas G.D., Androulakis G.S. and Vrahatis M.N., Global search methods for neural network training. Advances in Intelligent Systems and Computer Science, N.E. Mastorakis ed., World Scientific and Engineering Society Press, pp.47-52, 1999. 
  33. Magoulas G.D. and Vrahatis M.N., A model for local convergence analysis of batch-type training algorithms with adaptive learning rates. Recent Advances in Circuits and Systems, Mastorakis, N. E. (ed.), World Scientific, pp. 321-326, 1998. 
  34. Magoulas G.D., Vrahatis M.N., Grapsa T. N. and Androulakis G.S., A training method for discrete multilayer neural networks. Mathematics of Neural Networks: Models, Algorithms & Applications, Ellacot, S. W., Mason J. C. and I. J. Anderson (eds.), Kluwer Academic Publishers, Operations Research/Computer Science Interfaces series, chapter 41, pp. 245-249, 1997. 
  35. Magoulas G.D., Vrahatis M.N., Grapsa T. N. and Androulakis G.S., Neural network supervised training based on a dimension reducing method. Mathematics of Neural Networks: Models, Algorithms & Applications, Ellacot, S. W., Mason, J. C. and Anderson, I. J. (eds.), Kluwer Academic Publishers, Operations Research/Computer Science Interfaces series, chapter 42, pp.250-254, 1997. 
  36. Androulakis G.S., Magoulas G.D. and Vrahatis M.N., Minimization techniques in neural network supervised training. Selected Works of the 6th International Colloquium on Differential Equations, VSP International Science Publishers, pp. 9-16, 1996.


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