Dr. David Windridge is Associate Professor in Computer Science at Middlesex and heads the University's Data Science activities. His research interests centre on the related fields of machine-learning (A.I.), cognitive systems and computer vision (he also has a former research interest in astrophysics having obtained his Ph.D. in Cosmology at the University of Bristol).
He has authored and played a leading role on a number of large-scale machine-learning projects in academic and industrial research settings (including the EU DREAMS4CARS, EPSRC ACASVA and EU DIPLECS projects). He is also interested in cross-over areas of data science and has won a number of interdisciplinary research grants in areas as diverse as psychological modelling and proteomic classification. He is a Visiting Professor at Trento University, Italy, and a visiting Senior Lecturer at the University of Surrey. He has authored more than 100 peer-reviewed publications.
Bhattacharjee, Kamanasish and Kumar, Sushil and Pandey, Hari Mohan and Pant, Milli and Windridge, David and Chaudhary, Ankit (2018) An improved block matching algorithm for motion estimation in video sequences and application in robotics. Computers & Electrical Engineering . ISSN 0045-7906 (Accepted/In press)
Pandey, Hari Mohan and Windridge, David (2018) A comprehensive classification of deep learning libraries. In: International Congress on Information and Communication Technology, 27-28 Feb 2018, London. (Accepted/In press)
Di Pierro, Alessandra and Mengoni, Riccardo and Nagarajan, Rajagopal and Windridge, David (2017) Hamming distance kernelisation via topological quantum computation. In: 6th International Conference on the Theory and Practice of Natural Computing (TPNC 2017, 18-20 Dec 2017, Prague, Czech Republic. (Published online first)
Windridge, David (2017) Emergent intentionality in perception-action subsumption hierarchies. Frontiers in Robotics and AI, 4 . ISSN 2296-9144 (Published online first)
Attfield, Simon and Hewitt, Daniel and Xu, Kai and Passmore, Peter J. and Wagstaff, Adrian and Phillips, Graham and Windridge, David and Dash, Greg and Chapman, Richard and Mason, Lee (2016) Addressing VAST 2016 mini challenge 2 with POLAR kermode, classifier, excel on a power wall and data timelines. In: IEEE VAST Challenge 2016, 23 Oct 2016, Baltimore, MD, USA.