- George Magoulas
- 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).
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.