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London, United Kingdom
I conduct interdisciplinary research in software environments that exhibit different levels of intelligence and are capable of learning from data. My latest projects are in deep learning for psychophysiological data modelling and classification, and in intelligent learning environments. In these projects, I lead research on the development of intelligent components, which employ machine learning, sometimes combined with knowledge engineering methods, and the design and implementation of personalisation technologies. I was educated at the School of Engineering, University of Patras, Greece (BEng/MEng, Dr. Eng), and hold a PGCE from Brunel University, UK. Before joining academia I held R&D positions in the cement and automotive industries where I worked on the development of embedded systems employing soft computing and machine learning methodologies. My research received best paper awards from 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 Association for Computing Machinery (2009) and KES International (2010).

Journal papers


  1. Haq A. ul , Magoulas G.D., Jamal A., Majeed A., Sloan D., Users Perceptions of E-learning Environments  and Services Effectiveness: The Emergence of the Concept Functionality Model, Journal of Enterprise Information Management, forthcoming. 
  2. Cocea M. and Magoulas G.D., Design and evaluation of a case-based system for modelling exploratory learning behaviour of math generalisation, IEEE Transactions Learning Technologies, 02 February 2017, DOI: 10.1109/TLT.2017.2661310.
  3. Adam S.P., Magoulas G.D., Karras D.A., Vrahatis M.N, Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach, Journal of Machine Learning Research, 17(169), 1−40, 2016. 
  4. Karoudis K., Magoulas G.D., Ubiquitous Learning Architecture to Enable Learning Path Design across the Cumulative Learning Continuum, Informatics 2016, 3(4), 19; doi:10.3390/informatics3040019 
  5. Papanikolaou K.A., Makrh K., Magoulas G.D., Chinou D., Georgalas A., Roussos P., Synthesizing Technological and Pedagogical Knowledge in Learning Design: a Case Study in Teacher Training on Technology Enhanced Learning, International Journal of Digital Literacy and Digital Competence, 7 (1), 19-32, January-March 2016. 
  6. Sikora T., Magoulas G.D., Evolutionary Approaches to Signal Decomposition in an Application Service Management System, Soft Computing, 20 (8), 3063-3084, 2016. 
  7. Cocea M. and Magoulas G.D., Participatory Learner Modelling Design: a Methodology for Iterative Learner Models Development, Information Sciences, 321, 48–70, November 2015. 
  8. Adam S.P., Karras D.A., Magoulas G.D., Vrahatis M.N, Solving the linear interval tolerance problem for weight initialization of neural networks, Neural Networks, 54, 17–37, June 2014. 
  9. Sikora T., Magoulas G.D. Neural adaptive control in application service management environment, Evolving Systems Journal, 4(4), 267-287, 2013. 
  10. Laurillard, D., Charlton, P., Craft, B., Dimakopoulos, D., Ljubojevic, D., Magoulas, G., Masterman, E., Pujadas, R., Whitley, E.A., Whittlestone, K., A constructionist learning environment for teachers to model learning designs, Journal of Computer Assisted Learning, 29(1), 15–30, 2013. 
  11. Gutierrez-Santos S., Mavrikis M., and Magoulas G.D., A Separation of Concerns for Engineering Intelligent Support for Exploratory Learning Environments, Journal of Research and Practice in Information Technology, 44(3), 347-360, 2012. 
  12. Charlton P., Magoulas G. and Laurillard D., Enabling Creative Learning Design through Semantic Technologies, Technology, Pedagogy and Education, 21(2), 231-253, 2012. 
  13. Cocea M., Magoulas G.D., User Behaviour-driven Group Formation through Case-based Reasoning and Clustering, Expert Systems with Applications, 39(10), 8756-8768, 2012. 
  14. Noss R., Poulovassilis A., Geraniou E., Gutierrez-Santos S., Hoyles C., Kahn K., Magoulas G.D., Mavrikis M., The design of a system to support exploratory learning of algebraic generalisation, Computers and Education, 59(1), 63–81, 2012. 
  15. Peng C.-C. and Magoulas G.D., Nonmonotone Levenberg-Marquardt Training of Recurrent Neural Architectures for Processing Symbolic Sequences, Neural Computing and Applications, 20(6), 897-908, 2011. 
  16. Peng C.-C. and Magoulas G.D., Nonmonotone BFGS-trained Recurrent Neural Networks for Temporal Sequence Processing, Applied Mathematics and Computation, 217(12), 5421-5441, 2011. 
  17. de Freitas S., Rebolledo-Mendez G., Liarokapis F., Magoulas G., Poulovassilis A., Learning as immersive experiences: Using the four-dimensional framework for designing and evaluating immersive learning experiences in a virtual world, British Journal of Educational Technology, 41(1), 69-85, 2010. 
  18. Cocea M., Magoulas G.D., Hybrid Model for Learner Modelling and Feedback Prioritisation in Exploratory Learning, International Journal of Hybrid Intelligent Systems, 6(4), 211-230, 2009. 
  19. Dimakopoulos D.N. and Magoulas G. D., Interface design and evaluation of a personal information space for mobile learners, International Journal of Mobile Learning and Organisation, vol.3(4), 440 – 463, 2009. 
  20. Peng C.-C. and Magoulas G.D., Advanced Adaptive Nonmonotone Conjugate Gradient Training Algorithm for Recurrent Neural Networks, International Journal of Artificial Intelligence Tools, vol. 17(5), 963-984, 2008. 
  21. Anastasiadis A.D., Magoulas G.D., Particle Swarms and Nonextensive Statistics for Nonlinear Optimisation, The Open Cybernetics and Systemics Journal, vol. 2, 173-179, 2008. 
  22. de Freitas S., Harrison I., Magoulas G.D., Mee A., Mohamad F., Oliver M., Papamarkos G., Poulovassilis A., The Development of a System for Supporting the Lifelong Learner, British Journal of Educational Technology, 37(6), pp 867-880, 2006. 
  23. O'Neill P.D., Magoulas G.D., Liu X. Applying Wave Processing Techniques to Clustering of Gene Expressions, Journal of Intelligent Systems, vol. 15(1-4), 107–128, 2006. 
  24. Anastasiadis A. and Magoulas G.D., Analysing the Localisation Sites of Proteins through Neural Networks Ensembles, Neural Computing & Applications, vol. 15(3), 277 – 288, 2006. 
  25. Anastasiadis A., Magoulas G.D., and Vrahatis M.N, Improved sign-based learning algorithm derived by the composite nonlinear Jacobi process, Journal of Computational and Applied Mathematics, vol. 191, 166 – 178, 2006. 
  26. Anastasiadis A. and Magoulas G.D., Evolving Stochastic Learning Algorithm based on Tsallis Entropic index, The European Physical Journal B, vol. 50, 277–283, 2006. 
  27. Frias-Martinez E., Magoulas G. D., Chen S. Y., Macredie R. D., Automated User Modeling for Personalized Digital Libraries, International Journal of Information Management, vol. 26(3), 179-260, 2006. 
  28. Magoulas G.D., Anastasiadis A.D., Approaches to Adaptive Stochastic Search Based on the Nonextensive q-Distribution, International Journal of Bifurcation and Chaos, Vol. 16, No. 7, 2081-2091, 2006. 
  29. Magoulas G. D., Neuronal networks and textural descriptors for automated tissue classification in endoscopy, Oncology Reports, vol. 15, 997-1000, 2006. 
  30. Magoulas G. and Vrahatis M.N., Adaptive Algorithms for Neural Network Supervised Learning: A Deterministic Optimization Approach, International Journal of Bifurcation and Chaos, vol. 16(7), 1929–1950, 2006. 
  31. Plagianakos, V. P., Magoulas G. D. and Vrahatis M. N., Evolutionary training of hardware realizable multilayer perceptrons, Neural Computing & Applications, vol. 15(1), 33-40, 2006. 
  32. Plagianakos, V. P., Magoulas G. D. and Vrahatis M. N., Distributed Computing Methodology for Training Neural Networks in an Image-guided Diagnostic Application, Computer Methods and Programs in Biomedicine, vol. 81(3), 228-235, 2006. 
  33. Anastasiadis A., Magoulas G.D., and Vrahatis M.N., New Globally Convergent Training Scheme Based on the Resilient Propagation Algorithm, Neurocomputing, vol. 64, 253-270, March, 2005. 
  34. Anastasiadis A., Magoulas G. D., and Vrahatis M.N, Sign-based Learning Schemes for Pattern Classification, Pattern Recognition Letters, vol. 26, 1926–1936, 2005. 
  35. Chen S.Y., Magoulas G.D., and Dimakopoulos D., A Flexible Interface Design for Web Directories to Accommodate Different Cognitive Styles, Journal of the American Society for Information Science and Technology, vol. 56(1), 70-83, 2005. 
  36. Frias-Martinez E., Magoulas G., Chen S., Macredie R. , Modeling Human Behavior in User-Adaptive Systems: Recent Advances Using Soft Computing Techniques, Expert Systems with Applications, vol. 29(2), 320–329, 2005. 
  37. Ghinea G., Magoulas G.D., and Siamitros C., Multi-criteria Decision Making for Enhanced Perception-based Multimedia Communication, IEEE Tr. Systems, Man and Cybernetics: part A, vol. 35(6), 855-866, 2005. 
  38. Ghinea G., Magoulas G.D., and Siamitros C., Intelligent Synthesis Mechanism for Deriving Streaming Priorities of Multimedia Content, IEEE Tr. Multimedia, vol. 7(6), 1047-1053, 2005. 
  39. Ghinea G., Thomas J. P., Magoulas G.D., and Heravi S., Adaptation as a premise for perceptual-based multimedia communications, Int. J. Information Technology and Management, vol. 4(4), 405-422, 2005. 
  40. Stathacopoulou R., Magoulas G. D., Grigoriadou M. and Samarakou M., Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis, Information Sciences, vol. 170(2), 273-307, 2005. 
  41. Anastasiadis A., and Magoulas G.D., Nonextensive statistical mechanics for hybrid learning of neural networks, Physica A: Statistical Mechanics and its Applications, vol. 344, 372-382, 2004. 
  42. Chen S., Magoulas G.D. and Macredie R. Cognitive Styles and Users’ Reponses to Structured Information Representation, International Journal on Digital Libraries, vol. 4(2), 93-107, 2004. 
  43. Ghinea G., Magoulas G. D. and Frank A.O. Intelligent protocol adaptation in a medical e-collaboration environment, International Journal of Artificial Intelligence Tools, Vol. 13(1), 199-218, 2004. 
  44. Ghinea G., Magoulas G. D., and Frank A. O., Intelligent Multimedia Communication for Enhanced Medical e-Collaboration in Back Pain Treatment, Transactions of Institute Measurement Control, vol. 26(3), 223-244, 2004. 
  45. Magoulas G.D., Karkanis S.A., Karras D.A. and Vrahatis M.N., Evaluation of texture-based schemes in neural classifiers training, WSEAS Transactions on Computers, vol. 3(6), 1729-1735, December 2004. 
  46. Magoulas G.D., Plagianakos V.P., and Vrahatis M.N., Neural Network-based Colonoscopic Diagnosis Using On-line Learning and Differential Evolution, Applied Soft Computing, Vol. 4(4), 369-379, 2004. 
  47. Hossain S., Pouloudi A., Magoulas G.D. and Grigoriadou M., IT Adoption in British and Greek Secondary Education: Issues and Reflections, Themes in Education, vol. 4(2), 123-154, 2003. 
  48. Magoulas G.D., Papanikolaou K.A., and Grigoriadou M., Adaptive web-based learning: accommodating individual differences through system’s adaptation, British Journal of Educational Technology, vol. 34(4), 511 – 527, 2003. 
  49. O’Neill P., Magoulas G. D., and Liu X., Improved Processing of Microarray Data using Image Reconstruction Techniques, IEEE Tr. Nanobioscience, vol. 2(4), 176-183, 2003. 
  50. Papanikolaou K., Grigoriadou M., Kornilakis H., and Magoulas G.D., Personalising the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE, User-Modeling and User-Adapted Interaction, vol. 13, 213-267, 2003. 
  51. Vrahatis M.N., Magoulas G.D. and Plagianakos V.P., From linear to nonlinear iterative methods, Applied Numerical Mathematics, vol. 45(1), 59 - 77, 2003. 
  52. Magoulas G.D., Plagianakos V.P., and Vrahatis M.N., Globally convergent algorithms with local learning rates, IEEE Tr. Neural Networks, vol. 13(3), 774-779, 2002. 
  53. Papanikolaou K., Grigoriadou M., Magoulas G.D., and Kornilakis H., Towards New Forms of Knowledge Communication: the Adaptive Dimension of a Web-based Learning Environment, Computers and Education, vol. 39, 333-360, 2002. 
  54. Plagianakos V. P., Magoulas G.D., and Vrahatis M.N., Deterministic Nonmonotone Strategies for Effective Training of Multi-layer Perceptrons, IEEE Tr. Neural Networks, vol. 13(6), 1268-1284, 2002. 
  55. Magoulas G.D. , Papanikolaou K.A., and Grigoriadou M. Neurofuzzy Synergism for Planning the Content in a Web-based Course, Informatica, vol. 25, 39-48, 2001. 
  56. Magoulas G.D., Plagianakos G.D., Androulakis G.S. and Vrahatis M.N., A Framework for the Development of Globally Convergent Adaptive Learning Rate Algorithms, International Journal of Computer Research, vol. 10(1), 1-10, 2001. 
  57. Magoulas G.D., Plagianakos V.P. and Vrahatis M.N., Adaptive stepsize algorithms for on-line training of neural networks, Nonlinear Analysis: Theory, Methods and Applications, vol. 47, 3425-3430, 2001. 
  58. Parsopoulos K.E. , Plagianakos V.P. , Magoulas G.D. and Vrahatis M.N., Objective function ``stretching’’ to alleviate convergence to local minima, Nonlinear Analysis: Theory, Methods and Applications, vol. 47, 3419-3424, 2001. 
  59. Plagianakos V.P. , Magoulas G.D. and Vrahatis M.N. , Learning in multilayer perceptrons using global optimization strategies, Nonlinear Analysis: Theory, Methods and Applications, vol. 47, 3431-3436, 2001. 
  60. Karkanis S., Magoulas G.D. and Theofanous N., Image Recognition and Neuronal Networks: Intelligent Systems for the Improvement of Imaging Information, Minimally Invasive Therapy and Allied Technologies, vol. 9(3-4), 225-230, August 2000. 
  61. Magoulas G.D. and Vrahatis M.N., A Class of Adaptive Learning Rate Algorithms Derived by One-Dimensional Subminimization Methods, Neural, Parallel and Scientific Computations, vol. 8, 147-168, 2000. 
  62. Pouloudi A. and Magoulas G.D. , Neural Expert Systems in Medical Image Interpretation: Development, Use and Ethical Issues, Journal of Intelligent Systems, vol.10 (5-6), 451-471, 2000. 
  63. Vrahatis M.N., Androulakis G.S., Lambrinos J.N. and Magoulas G.D., A class of gradient unconstrained minimisation algorithms with adaptive stepsize, Journal of Computational and Applied Mathematics, vol. 114, 367-386, 2000. 
  64. Vrahatis M.N., Magoulas G.D. and Plagianakos V.P., Globally convergent modification of the Qprop method, Neural Processing Letters, vol. 12(2), 159-170, October 2000. 
  65. Magoulas G.D., Vrahatis M.N. and Androulakis G.S., Improving the convergence of the back-propagation algorithm using learning rate adaptation methods, Neural Computation, vol. 11, 1769-1796, 1999. 
  66. Androulakis G.S., Magoulas G.D. and Vrahatis M.N., Geometry of learning: visualizing the performance of neural network supervised training methods, Nonlinear Analysis: Theory, Methods and Applications, vol. 30, 4539-4544, 1997. 
  67. Magoulas G.D., Vrahatis M.N. and Androulakis G.S., Effective back-propagation training with variable stepsize, Neural Networks, vol.10, 69-82, 1997. 
  68. Magoulas G.D., Vrahatis M.N. and Androulakis G.S., On the alleviation of the problem of local minima in back-propagation, Nonlinear Analysis: Theory, Methods and Applications, vol. 30, 4545-4550, 1997. 
  69. Vrahatis M.N., Androulakis G.S. and Magoulas G.D., On the acceleration of the back-propagation training algorithm, Nonlinear Analysis: Theory, Methods and Applications, vol. 30, 4551-4554, 1997. 
  70. King R.E., Magoulas G.D. and Stathaki A.A., Multivariable fuzzy controller design, Control Engineering Practice, vol.2, 431-437, 1993. 
  71. Magoulas G.D., King R.E. and Stathaki A.A., Design of industrial multivariable fuzzy controllers, Studies in Informatics and Control, vol.2, 253-261, 1993.