- 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).
3 Mar 2017
Deep Learning Parkinson’s from Smartphone Data
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.