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George Magoulas
George has a BEng/MEng (Systems & Control) and a PhD (Neural Networks Learning) from the Electrical and Computer Engineering Department of the University of Patras, Greece, and a PGCE (Higher Education) from Brunel University. He developed embedded systems for the cement (1990-1993) and automotive (1997-1998) industries using soft computing and computational intelligence methodologies, and held researh and academic posts at the University of Athens and Brunel University before coming to Birkbeck in 2004.
Over the past 15 years, he has researched synergistic approaches to foster the development of new theory and solve real problems by means of the theoretical tools developed. This process is particularly important to address some long-standing challenges in the area of bio-inspired Intelligent Systems that involve learning, adaptation and evolution in complex environments. He is perhaps best recognised for his work in learning and adaptation algorithms. Specifically, his work includes contributions towards: (i) the design of novel learning algorithms with improved characteristics for artificial neural networks; (ii) a better understanding of the convergence behaviour of some popular learning algorithms; and (iii) the design of user modelling and adaptation techniques within the framework of Computational Intelligence.
His research has been published in some of the top journals in the area of computational intelligence, presented in key international conferences and cited in text books. In this area, recent work targets modelling and adaptation in complex environments and has led so far to a series of publications in leading international journals, and awards at International Conferences (IEEE, ACM, IADIS); a list of publications and research awards is provided on his personal webpage.
He has also developed techniques that have been extended to new contexts and application areas, and formed the basis for work in the area of adaptive systems. Relating to adaptive and personalised systems in particular, he developed computational intelligence-based techniques for learner modelling in personalised learning environments that were used in real-world settings, such as in the case of the personalised learning systems INSPIRE and MiGen, and for resources management in user-adaptive systems.
After joining Birkbeck in 2004, personalised learning systems became the focus of his work since he was particularly asked to further develop the School’s research in this area, as an active member of the London Knowledge Lab.
The contribution and value of his research at national and international levels is also reflected in the number of research awards and grants. In his career so far he has secured 1.2M+ GBP funding from UK funding agencies as Principal Investigator (PI) or co-Investigator (co-I) of research and development projects that have attracted a total of 3.5M+ GBP in competitive external funding: this includes research grants from the EPSRC (PI of the JiT-Pie project), the ESRC (co-I of the MiGen project, the Workplace Personalised Learning Environments project, and the Learning Design Support Environment project), the AHRC (co-I of the Cognitive Personalised Interfaces project), and the JISC (PI of the L4All and MyPlan projects). Within these and other projects, he has worked closely with partners active in various areas of information technology and technology enhanced learning, such as the Institute of Education, Oxford University, and the London School of Economics. He was involved in collaborations with public bodies, such as Northwick Park Hospital and the Institute of Medical Research, the Linking London Lifelong Learning Network, the UCAS and the LearnDirect. He has developed collaborations with various academic institutions in UK and abroad and gained significant experience in leading and managing teams of researchers and practitioners working in software systems projects as well as liaising with teams of external consultants and contractors. Lastly, in 2010 he was invited to join the EPSRC Peer Review College.
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