- 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).
13 Mar 2017
AI-based Mobile App Tests for Parkinson’s in Minutes: our work on Deep Learning for detecting useful tremor signals from smartphone data features in a recent NVIDIA Developer News article.
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.
3 Jan 2017
My group received an NVIDIA Grant to support research on Deep Learning Ensembles. In this project, Alan Mosca will investigate novel methods for efficient creation and training of deep learning ensembles, and will develop tools for parallel training of deep learning ensembles on multiple GPUs.