Staff profile

I am an Associate Professor in Computer Science at Durham University and a member of the Scientific Computing and VIVID groups. My research is in theoretical areas of Deep Learning, specifically looking at Deep Generative Models. Previously, I co-founded a Durham University startup deploying large AI models in healthcare. Please visit my Website for more information about my research or visit my Google Scholar for a full ist of publications.

Research Highlights

Research within my group has led to the invention of Unleashing Transformers (ECCV 2022), which generate diverse high-resolution samples. We also invented Gradient Origin Networks (ICLR 2021), which show that you don't need to use encoders in autoencoders (here is a video by Yannic Kilcher on GONs). We've published Deep Generative Modelling: A Comparative Review (IEEE TPAMI 2021) with interest in non-adversarial models such a Denoising Diffusion Models for Anomaly Detection AnoDDPM (CVPR NTIRE 2022) and Unpaired Translation UNIT-DDPM. In other domains, we developed an extension of ray tracing for protein docking called Shape Tracing (IEEE BIBE 2020) and an end-to-end solution for Deep Learning Protein Conformational Space (PRX 2021). Our interdisciplinary research has also been applied in unsupervised Medical Anomaly Detection (IEEE ISBI 2021), Cross-Domain Imagery (ICPR 2021), Multi-view Transformers for Object Detection, and to generate 3D CT-like Images from 2D X-rays MedNeRF (IEEE EMBC 2022).

Teaching

I teach the L3 Deep Learning module (COMP3547), the Reinforcement Learning module and the year two Cyber Security submodule (COMP2211). Slides and other material are available in the Teaching section of my website. I also have a YouTube channel with a lot of Deep Learning and Reinforcement Learning Material.

Professional Activities

I am an Area Chair for BMVC and also the Admissions Tutor for Computer Science. In the past have been the Open Day coordinator and have been an invited speaker at several conferences and universities, including the 2023 national DICE conference, and the Chinese University of Hong Kong (CUHK). I was a speaker on BBC Sunday Politics about Cyber Security spending in public bodies, and I am also a reviewer for the EU Commission, CVPR, and IEEE including TPAMI, TIFS, TNNLS, TIP and TMI.

Conference Paper

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Bond-Taylor, S., & Willcocks, C. G. (2021, May). Gradient Origin Networks. Presented at International Conference on Learning Representations, Vienna / Virtual
• 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
• 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)
• 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
• 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

Presentation

• 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