Staff profile

Chris G. Willcocks is an associate professor in computer science, where he specialises in generative models and machine reasoning. He has authored 35+ peer-reviewed publications in world-leading conferences/journals within computer science, applied mathematics, and security, including ICLR, TPAMI, CVPR, ECCV, ICCV and TIFS. More information is available on his website and a full list of his publications is on Google scholar.

Research Highlights

The group's recent theoretical work, ∞-diff (ICLR 2024), demonstrated diffusion models in an infinite-dimensional Hilbert space for arbitrary resolution synthesis. They also released a highly cited comparative review on deep generative models (TPAMI 2022) and proposed strategies that improve frontier AI sampling, such as GPT-4. He also developed gradient origin networks (ICLR 2021), showing encoders are often unnecessary in autoencoders (see Yannic Kilcher's video on GONs). He is internationally recognised for unsupervised anomaly detection, including AnoDDPM (CVPR 2022), and has applied diffusion models in unpaired translation. The group's research has also been applied in unsupervised medical anomaly detection (IEEE ISBI 2021), cross-domain imagery (ICPR 2021), multi-view transformers for object detection, generating 3D CT-like images from 2D X-rays MedNeRF (IEEE EMBC), and in threat detection (IEEE TIFS).

Undergraduate Teaching

He teaches the deep learning and reinforcement learning modules and the year two cyber security submodule. Slides and other material are available in the teaching section of his website. He also has a YouTube channel with deep learning and reinforcement learning material.

Industry Engagement

His research has been applied commercially, collaborating with multinationals and SMEs, including P&G, Unilever, Dyson, Heidelberg Engineering, AstraZeneca, Gliff.ai, Scott Logic, and Waterstons, as well as the public sector: the NCA, NERCCU, DASA, DSTL, and the NHS. He is a fellow of the HEA, and has delivered over 15 invited talks and participated in public discussions on ethics and cyber security with Microsoft and engaged with the UK Cabinet Office. In 2016, he co-founded a Durham University research spinout following successful InnovateUK seed funding for a high-growth AI SME, and led the research team in the early stages.

Professional Activities

He serves as area chair for BMVC, is the admissions tutor for computer science, and is a member of the scientific computing group. In the past, he has been the open day coordinator and has been an invited speaker at several conferences and universities, including the 2023 and 2024 national DICE conferences, and the Chinese University of Hong Kong (CUHK). He was a speaker on BBC sunday politics about cyber security spending in public bodies, and is a reviewer for CVPR, ICLR, the EU commission, and IEEE including TPAMI, TIFS, TNNLS, TIP and TMI.

Research Interests

His research interests are in theoretical generative modelling, machine reasoning and frameworks for AI safety. If you are interested in joining his research group and have a background in mathematics, computer science, engineering or physics, please see the information here and email to discuss.

Conference Paper

• Bond-Taylor, S., & Willcocks, C. G. (2024, May). ∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States. Presented at The International Conference on Learning Representations (ICLR), Vienna Austria
• Corona-Figueroa, A., Shum, H. P. H., & Willcocks, C. G. (2024, June). Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington
• Liu, M., Frawley, J., Wyer, S., Shum, H. P. H., Uckelman, S. L., Black, S., & Willcocks, C. G. (2024, June). Self-Regulated Sample Diversity in Large Language Models. Presented at NAACL 2024: 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Mexico City
• Isaac-Medina, B., Willcocks, C., & Breckon, T. (2023, June). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC
• Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023, October). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. Presented at ICCV23: 2023 IEEE/CVF International Conference on Computer Vision, Paris, France
• Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T., & Willcocks, C. (2022, October). Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes. Presented at ECCV 2022: European Conference on Computer Vision, Tel Aviv, Israel
• Isaac-Medina, B., Willcocks, C., & Breckon, T. (2022, August). Multi-view Vision Transformers for Object Detection. Presented at International Conference on Pattern Recognition, Montreal, Canada
• Isaac-Medina, B., Bhowmik, N., Willcocks, C., & Breckon, T. (2022, June). Cross-modal Image Synthesis in Dual-Energy X-Ray Security Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana
• Wyatt, J., Leach, A., Schmon, S. M., & Willcocks, C. G. (2022, June). AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, New Orleans, LA
• Leach, A., Schmon, S. M., Degiacomi, M. T., & Willcocks, C. G. (2022, April). Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment. Presented at ICLR 2022 Workshop on Geometrical and Topological Representation Learning
• Corona-Figueroa, A., Frawley, J., Bond-Taylor, S., Bethapudi, S., Shum, H. P., & Willcocks, C. G. (2022, July). MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray. Presented at 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland
• Sunal, C. E., Willcocks, C. G., & Obara, B. (2021, January). Real Time Fencing Move Classification and Detection at Touch Time during a Fencing Match. Presented at International Conference on Pattern Recognition (ICPR), Milan
• Nguyen, B., Feldman, A., Bethapudi, S., Jennings, A., & Willcocks, C. G. (2021, April). Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting. Presented at 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France
• Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2021, December). Robust 3D U-Net Segmentation of Macular Holes. Presented at The 29th Irish Conference on Artificial Intelligence and Cognitive Science 2021, Dublin, Republic of Ireland, December 9-10, 2021, Dublin, Ireland
• Isaac-Medina, B., Willcocks, C., & Breckon, T. (2021, January). Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy
• Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021, October). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. Presented at 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada
• Sasaki, H., Willcocks, C., & Breckon, T. (2021, January). Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy
• Bond-Taylor, S., & Willcocks, C. G. (2021, May). Gradient Origin Networks. Presented at International Conference on Learning Representations, Vienna / Virtual
• Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2020, October). Segmentation of macular edema datasets with small residual 3D U-Net architectures. Presented at 20th IEEE International Conference on BioInformatics and BioEngineering, Cincinnati, OH
• Leach, A., Rudden, L. S., Bond-Taylor, S., Brigham, J. C., Degiacomi, M. T., & Willcocks, C. G. (2020, December). Shape tracing: An extension of sphere tracing for 3D non-convex collision in protein docking. Presented at 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)
• Medhat, F., Mohammadi, M., Jaf, S., Willcocks, C., Breckon, T., Matthews, P., McGough, A. S., Theodoropoulos, G., & Obara, B. (2018, December). TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text. Presented at IEEE International Conference on Big Data., Seattle, WA, USA

Doctoral Thesis

• Willcocks, C. G. (2013). Sparse Volumetric Deformation - Animating and rendering huge amounts of volumetric data using GPGPU computing. (Thesis). Durham University. https://durham-repository.worktribe.com/output/1617928

Journal Article

• Duan, H., Long, Y., Wang, S., Zhang, H., Willcocks, C. G., & Shao, L. (2023). Dynamic Unary Convolution in Transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 12747 - 12759. https://doi.org/10.1109/tpami.2022.3233482
• Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2021). Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7327-7347. https://doi.org/10.1109/tpami.2021.3116668
• Alhasson, H., Willcocks, C. G., Alharbi, S. S., Kasim, A., & Obara, B. (2021). The relationship between curvilinear structure enhancement and ridge detection approaches. Visual Computer, 37(8), 2263-2283. https://doi.org/10.1007/s00371-020-01985-4
• Ramaswamy, V. K., Musson, S. C., Willcocks, C. G., & Degiacomi, M. T. (2021). Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations. Physical Review X, 11(1), Article 011052. https://doi.org/10.1103/physrevx.11.011052
• Willcocks, C. G., Jackson, P. T., Nelson, C. J., Nasrulloh, A., & Obara, B. (2019). Interactive GPU Active Contours for Segmenting Inhomogeneous Objects. Journal of Real-Time Image Processing, 16(6), 2305-2318. https://doi.org/10.1007/s11554-017-0740-1
• Alharbi, S. S., Willcocks, C., Jackson, P. T., Alhasson, H. F., & Obara, B. (2019). Sequential graph-based extraction of curvilinear structures. Signal, Image and Video Processing, 13(5), 941-949. https://doi.org/10.1007/s11760-019-01431-6
• Akcay, S., Kundegorski, M., Willcocks, C., & Breckon, T. (2018). Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery. IEEE Transactions on Information Forensics and Security, 13(9), 2203-2215. https://doi.org/10.1109/tifs.2018.2812196
• Nasrulloh, A., Willcocks, C., Jackson, P., Geenen, C., Habib, M., Steel, D., & Obara, B. (2018). Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes. IEEE Transactions on Medical Imaging, 37(2), 580-589. https://doi.org/10.1109/tmi.2017.2767908
• Willcocks, C., Jackson, P. T., Nelson, C. J., & Obara, B. (2016). Extracting 3D parametric curves from 2D images of helical objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1757-1769. https://doi.org/10.1109/tpami.2016.2613866
• Willcocks, C. G., & Li, F. W. (2012). Feature-Varying Skeletonization. Visual Computer, 28(6-8), 775-785. https://doi.org/10.1007/s00371-012-0688-x