Projects per year
Personal profile
Research interests
I am a Royal Society Industry Fellow and work in the Department of Computer Science as a Reader (Associate Professor) in Visual Computing and Machine Learning. I am an investigator in the CAMERA research centre and the Visual Computing Group as well as a co-director of the university's research centre in Mathematics and Algorithms for Data.
I am interested in the modelling of shape and the use of machine learning techniques applied in the domain of computer vision (processing images from the real world) and graphics (creating and manipulating new images). Shape is such a fundamental component of graphics and vision that research in this field unifies the two subjects and there is obviously a great advantage to solving problems in both areas simultaneously since they help one-another. My latest ongoing research aims to learn automatically models of both man-made and natural shapes, and produce intelligent systems that make it easier to process, create and manipulate images. For further details please see my personal homepage for further details and publications.
Willing to supervise doctoral students
Please see the link here for details on open PhD and PostDoc positions in my group.
Education/Academic qualification
Engineering, Doctor of Philosophy, University of Cambridge
2006 → 2010
Award Date: 14 May 2011
Engineering, Master of Engineering, University of Cambridge
2002 → 2006
External positions
Honorary Associate Professor, University College London
2018 → …
Royal Society Industry Fellow, The Royal Society
2017 → …
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Network
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IAA – Embedded Machine Learning for the Open Flexure Microscope
Campbell, N. & Vodenicharski, B.
Engineering and Physical Sciences Research Council
1/11/22 → 30/04/23
Project: Research council
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EMIL: The European Media and Immersion Lab (Former EU Project)
Lutteroth, C., Campbell, N., Clarke, C. & O'Neill, E.
1/09/22 → 28/02/25
Project: EU Commission
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MAchine Learning And Rheumatic Diseases
Royal United Hospitals Bath NHS Foundation Trust
1/03/22 → 31/03/23
Project: Central government, health and local authorities
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My World - Strength in Places Fund
Cosker, D., Campbell, N. & Li, W.
1/04/21 → 31/03/26
Project: Central government, health and local authorities
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0
Cosker, D., Bilzon, J., Campbell, N., Cazzola, D., Colyer, S., Lutteroth, C., McGuigan, P., O'Neill, E., Proulx, M. & Yang, Y.
Engineering and Physical Sciences Research Council
1/11/20 → 31/10/25
Project: Research council
Research output
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Aligned Multi-Task Gaussian Process
Mikheeva, O., Kazlauskaite, I., Hartshorne, A., Kjellström, H., Ek, C. H. & Campbell, N., 2022.Research output: Contribution to conference › Paper › peer-review
File11 Downloads (Pure) -
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching
Chen, C. & Campbell, N., 24 Nov 2022.Research output: Contribution to conference › Paper › peer-review
Open Access -
Cell Anomaly Localisation using Structured Uncertainty Prediction Networks
Vodenicharski, B., Mcdermott, S., Webber, K., Introini, V., Cicuta, P., Bowman, R., Simpson, I. & Campbell, N., 2022.Research output: Contribution to conference › Paper › peer-review
File8 Downloads (Pure) -
Learning Structured Gaussians to Approximate Deep Ensembles
Simpson, I. J. A., Vicente, S. & Campbell, N. D. F., 30 Jun 2022, Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. IEEE, p. 366-374 9 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2022-June).Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding
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Learning Structured Gaussians to Approximate Deep Ensembles
Simpson, I., Vicente, S. & Campbell, N., 2022.Research output: Contribution to conference › Paper › peer-review
File8 Downloads (Pure)