- George Magoulas
- London, United Kingdom
- I conduct interdisciplinary research that seeks to enable systems to exhibit different levels of intelligence, learn from data and transfer knowledge to new contexts. My latest projects are in deep learning for psychophysiological data modelling and classification, and in intelligent learning environments. I design and develop innovative learning algorithms, system components that employ machine learning, sometimes combined with knowledge engineering, learner models and intelligent tutors. I was educated at the University of Patras, Greece (BEng/MEng, Dr. Eng), and hold a PGCE (Brunel University, UK). Before joining academia I held R&D positions in the cement and automotive industries working on embedded systems that used soft computing and machine learning methods. 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 ACM (2009) and KES International (2010). I am a Fellow of the Higher Education Academy, and a Member of the EPSRC College, UK.
9 Jun 2017
Boosted Residual Networks at the 18th EANN 2017
Distillation of Deep Learning Ensembles as a 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
Massive Open Online Courses in Software Engineering Education at the 47th Annual Frontiers in Education (FIE 2017) Conference
13 Mar 2017
3 Mar 2017
Deep Learning Parkinson’s from Smartphone Data will be presented at the International Conference on Pervasive Computing and Communications in Hawaii on March 14th 2017. The paper presents a deep learning feature for CloudUPDRS to distinguish between useful tremor data collected through smartphone sensors, and inaccurate, noisy or erroneous measurements. The project features in a recent New Scientist article.
10 Jan 2017
Design and Evaluation of Adaptive Feedback to Foster ICT Information Processing Skills in Young Adults at DigiLEarn@WWW2017.
Training Convolutional Networks with Weight–wise Adaptive Learning Rates at the 25th ESANN 2017.
Design and evaluation of a case-based system for modelling exploratory learning behaviour of math generalisation in IEEE TLT.
Deep Learning Parkinson’s from Smartphone Data at the IEEE PerCom 2017.