I am Professor in Data Science and Machine Learning at the Department of Computing, Middlesex University, London and founder of the university's Data Science Programme.
My research interests center on Machine Learning and Cognitive Systems, with a historic research interest in Astrophysics/Cosmology (stochastic cosmological modelling in particular).
Much of my machine learning research has focused on classifier & kernel fusion, for which I developed a number of approaches including the neutral point and tomographic methods. These and other methodological developments have been applied to various domains including medical analytics, computer vision, biometrics, autonomous vehicles and genomics. More recently, my research has branched into the areas of generative deep learning, neurosymbolics and quantum computing (I sit on the Editorial Board of the Springer journal Quantum Machine Intelligence).
My cognitive science activity centers on the twin notions of cognitive bootstrapping and perception-action learning, serving as both an idealised model of human cognition as well as a mechanism for machine learning. As such, they serve as an ideal approach to human-computer interfacing and digital twinning. (Perception-Action learning also has the ability, I have argued in a number of papers, to address a range of foundational issues in philosophy).
In pursing the above, I've played a leading role in a number of projects at the interface of machine learning and cognitive systems (including EPSRC ACASVA, EU DIPLECS & DREAMS4CARS), as well as a range of large scale industrial and academic pattern recognition projects. I liaise with companies across a diverse commercial range (e.g. FIAT and Sanofi) and regularly work with healthcare providers such as AlderHey Children's Hospital, as well as government bodies such as DSTL. I sit on the advisory boards of AutoM8 Tech Ltd & AIDizital and carry-out a range of consultancy activities for various SMEs.
I am a Visitor at the Centre for Vision, Speech & Signal Processing (CVSSP) at the University of Surrey UK.
I've authored around 200 peer-reviewed publications in the above subject areas; preprints may be found at:
http://scholar.google.co.uk/citations?hl=en&user=p0K-zIsAAAAJ&view_op=list_works&sortby=pubdate
https://www.researchgate.net/profile/David_Windridge
http://eprints.mdx.ac.uk/cgi/search/simple?_action_search=Search&_order=bytitle&basic_srchtype=ALL&_satisfyall=ALL&q=&_action_search=Search&q3=windridge
http://epubs.surrey.ac.uk/
Balla, Yashaswini and Tirunagari, Santosh and Windridge, David (2023) Machine learning in pediatrics: Evaluating challenges, opportunities, and explainability. Indian Pediatrics . S097475591600533. ISSN 0974-7559
Zammit, Omar and Smith, Serengul and Windridge, David and De Raffaele, Clifford (2023) Reducing the dependency of having prior domain knowledge for effective online information retrieval. Expert Systems , 40 (4). ISSN 0266-4720
Incudini, Massimiliano and Grossi, Michele and Ceschini, Andrea and Mandarino, Antonio and Panella, Massimo and Vallecorsa, Sofia and Windridge, David (2023) Resource saving via ensemble techniques for quantum neural networks. arXiv .
Incudini, Massimiliano and Grossi, Michele and Mandarino, Antonio and Vallecorsa, Sofia and Di Pierro, Alessandra and Windridge, David (2022) The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning. arXiv .
Nwegbu, Nnanyelugo and Tirunagari, Santosh and Windridge, David (2022) A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk. Scientific Reports , 12 (1). pp. 1-16. ISSN 2045-2322
