- 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.
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.