Professor David Hogg
- Position: Professor of Artificial Intelligence
- Areas of expertise: computer vision; machine learning
- Email: D.C.Hogg@leeds.ac.uk
- Phone: +44(0)113 343 5765
- Location: 2.21B Bragg Building
- Website: dblp computer science bibliography | Googlescholar | Researchgate | ORCID
David Hogg is Professor of Artificial Intelligence at the University of Leeds. He is internationally recognized for his work on computer vision, particularly in the areas of video analysis and activity recognition. He works extensively across disciplinary boundaries, applying AI in engineering design, biology, medicine and environmental sciences. He has been a visiting professor at the MIT Media Lab, Pro-Vice-Chancellor for Research and Innovation at the University of Leeds (2011-2016), Chair of the ICT Strategic Advisory Team at the Engineering and Physical Sciences Research Council (EPSRC) in the UK, Chair of an international review panel for Robotics and Artificial Intelligence commissioned by EPSRC (2017), and a Turing Fellow. Until 2018, he was Chair of the Academic Advisory Group of the Worldwide Universities Network (WUN), helping to promote collaborative research between over 20 prominent research intensive universities from around the globe. He is Director of the UKRI Centre for Doctoral Training in AI for Medical Diagnosis and Care; and a Co-Director of the Northern Pathology Imaging Co-operative. David is a Fellow of the European Association for Artificial Intelligence (EurAI), a Distinguished Fellow of the British Machine Vision Association, and a Fellow of the International Association for Pattern Recognition.
David is Director of the Artificial Intelligence research theme in the School of Computing, and Lab Director of the Computer Vision group.
A short talk on AI in the Public Sector
- Director of Artificial Intelligence research theme
- Director of UKRI Centre for Doctoral Training in AI for Medical Diagnosis and Care
David pioneered the use of three-dimensional geometric models for tracking flexible structures (e.g. the human body) in natural scenes, and contributed to establishing statistical approaches to learning of shape and motion as one of the pre-eminent paradigms in the field. Current research is on representation and learning of activities from video, specifically models of interaction, and applications of machine learning in science and engineering. Part of this work is exploring the integration of vision within a broader cognitive framework that includes audition, language, action, and reasoning.
See the Computer Vision group for details of our work in this area.
In process engineering, we are working on the use of image analysis to monitor and optimise crystal growth – EPSRC Shape4PPD (2021-2024)
In engineering design, we are developing ideas to accelerate the design process – EPSRC Design Configuration Spaces (2019-2022)
UKRI Innovate UK KTP with Vet-AI (2020-2022)
UKRI Innovate UK KTP with Scaled Insights (2020-2024)
Analysing the Motion of Biological Swimmers (2018-2020)
<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>
- BSc Applied Mathematics, Warwick, 1975
- MSc Computer Science, Western Ontario, 1976
- DPhil, Sussex, 1984
- IEEE Computer Society
- British Machine Vision Association (BMVA)
- The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB)
- International Association of Pattern Recognition (IAPR)
I teach in the School of Computing in the general areas of Artificial Intelligence, Machine Learning and Computer Vision.
Research groups and institutes
- Artificial Intelligence
Current postgraduate researchers
<li><a href="//phd.leeds.ac.uk/funding/49-ukri-centre-for-doctoral-training-in-artificial-intelligence-for-medical-diagnosis-and-care">UKRI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care</a></li>