Professor David Hogg
- Position: Professor of Artificial Intelligence
- Areas of expertise: computer vision; machine learning
- Email: D.C.Hogg@leeds.ac.uk
- Location: 2.21B Bragg Building
- Website: dblp computer science bibliography | Googlescholar | Researchgate | ORCID
Profile
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. At present, he is not taking on the supervision of new PhD students.
Responsibilities
- Director of Artificial Intelligence research theme
- Director of UKRI Centre for Doctoral Training in AI for Medical Diagnosis and Care
Research interests
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.
Selected recent publications and project pages
Talking Head from Speech Audio using a Pre-trained Image Generator
Deep learning of Parkinson's movement from video, without human-defined measures
Mapping the extent of giant Antarctic icebergs with deep learning
3D shape reconstruction of semi-transparent worms
Self-supervised 3d human pose estimation from a single image
Online perceptual learning and natural language acquisition for autonomous robots
Understanding the robustness of skeleton-based action recognition under adversarial attack
Unsupervised human activity analysis for intelligent mobile robots
Selected recent projects
Nowcasting with Artificial Intelligence for African Rainfall: NAIAR (2024-2027)
Forecasting volcanic activity using deep learning (DEEPVOLC) (2020-2026)
Towards intelligent engineering design systems (2019-2022)
Knowledge Transfer Partnership with Vet-AI (2020-2022)
Current PhD students
Caitlin Howarth
Claire Bartholomew
Mikael Down
Fangjun Li
Eilish O’Grady
Chen Jiang
Arpita Saggar
Project pages for some earlier research
A short talk on AI in the Public Sector
<h4>Research projects</h4> <p>Some research projects I'm currently working on, or have worked 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>Qualifications
- BSc Applied Mathematics, Warwick, 1975
- MSc Computer Science, Western Ontario, 1976
- DPhil, Sussex, 1984
Professional memberships
- 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)
Research groups and institutes
- Artificial Intelligence