Head of Group

• Carola-Bibiane Schönlieb

Administrative Support

• Zvezda Woodhouse

Research Team

• Ander Biguri: Tomographic reconstruction, particularly in Cone Beam Computed Tomography.
• Moshe Eliasof: Machine learning for image segmentation and processing.
• Erik Jansson: Gradient flows and shape matching for imaging applications, computational physics, and geometric numerics for partial differential equations and fluid dynamics.
• William Lee: Industrial Mathematics, Fluid Mechanics, and Heat and Mass Transfer.
• Chaoyu Liu: Machine learning for PDEs and image processing.
• Priscilla Canizares Martinez: Theoretical physics, machine learning, particularly in gravitational-wave astronomy.
• Davide Murari: Application of neural networks, dynamical systems, and approximation theory to image analysis, computational physics, and symplectic integration.
• Michael Roberts: Image processing, machine learning, health applications, translation into clinic.

Ph.D. Students

• Mary Chriselda Antony Oliver: Analysis of graph-based semi-supervised algorithms by calculus of variation and optimal transport.
• Yichen (Kelly) Chen: Multi-dimensional survival modelling, adverse event risk prediction in clinical trials (joint with AstraZeneca).
• Chun-Wun (Sam) Cheng: Implicit deep learning: PDEs, ODEs and beyond.
• Yanqi Cheng: Optimisation for inverse problem.
• Simon Deltadahl: Generative modelling, foundation models for healthcare, outbreak detection (in the BloodCounts! consortium).
• Jay Dhesi: Topological data analysis for quantifying relationships in the tumour microenvironment.
• Holly Houliston: Study of baleen whales in high-resolution satellite imagery.
• Kelly Kokka (Joint with the Public Health Modelling group): Biomedical imaging, machine learning.
• Yangming Li: Diffusion modelling.
• Alex Massucco: Calculus of variations for Deep Learning algorithms and structure preserving neural networks.
• Wallace Peaslee: Mathematics for the analysis of art and cultural heritage.
• James Rowbottom: Machine learning for PDEs.
• Andrea Sainz Bear: Computed Tomography (CT) and extreme low-dose CT reconstruction using machine learning.
• Zakhar Shumaylov: Geometric and physics informed deep learning for inverse problems.
• Thanh Trung (Troy) Vu: Attribute-based learning for histopathological image analysis.
• Ruodan Yan: Spatiotemporal graph neural networks for perfusion MRI in glioma patients.
• Fan Zhang: Federated learning for large-scale healthcare systems.

Visitors

• Honglei Brinkmann (University of Aachen): Image processing with deep learning method.
• Qinqian Lei (National University of Singapore): Human and Object Image Analysis.
• Georg Francis Barlaup Lovric (University of Bristol): Inverse problems for physics.
• Tingting Wu (Nanjing University of Posts and Telecommunications): Computer Vision, Numerical Optimization, Image Processing.

Former Members and Visitors

• Kweku Abraham: Theoretical guarantees for machine learning, frequentist validity of Bayesian procedures, false discovery rates, and hidden Markov models.
• Samar M. Alsaleh (Visiting PhD student)
• Jonathan Ang: Learning latent representations via a DeepWalk approach.
• Stefi Anita: Segmentation for radiotherapy treatment planning.
• Mickael Assaraf: Automated graph data augmentation.
• Marie Autume: Art restoration.
• Angelica I. Aviles-Rivero: Inverse problems, medical imaging, computational analysis and machine learning.
• Debmita Bandyopadhyay: Multi-sensor data analysis, forest species mapping, Alpine vegetation dynamics, statistical modelling, machine learning.
• Georgios Batzolis: Geometry of data, diffusion models, riemannian geometry.
• Martin Benning (Post-Doc)
• Hugo Blanc: Semi-supervised image classification for hyperspectral data.
• Nathan Blanken (Visiting PhD student)
• Yoeri Boink: Motion analysis.
• Nicolas Boulle: Numerical analysis and deep learning.
• Anna Breger: Mathematical image processing in medical problems with diverse data types, image quality assessment.
• Nina Dekoninck Bruhin: Functional data analysis in medical imaging.
• Andrey Bryutkin: Graph transformers for PDEs.
• Tatiana Bubba: Computational inverse problems with applications to medical imaging and spent nuclear fuels imaging.
• Thomas Buddenkotte: Deep learning, segmentation of high grade serous ovarian cancer on computed tomography images.
• Aurelie Bugeau: Image and video processing and analysis.
• Leon Bungert (Visiting PhD student)
• Xiaohao Cai (Post-Doc)
• Ziruo Cai: Uncertainty quantification in medical imaging, optimisation, machine learning.
• Luca Calatroni: PDE models for imaging problems and applications.
• Marcello Carioni: Inverse problems, machine learning, calculus of variations.
• Antonin Chambolle (Visiting scholar)
• Chu Chen: Solving inverse problems in dynamical systems for biomedical imaging, machine learning for tomographic reconstruction.
• Veronica Corona: Multi-spectral characterisation of thalamic nuclei with ultra-high field MRI.
• Jan Cross-Zamirski: Cross-modality profiling of high-content microscopy images with deep learning.
• Noemie Debroux (Post-Doc)
• Jim Denholm: Computer vision methods to analyse histopathological image data to coeliac disease.
• Zhongying Deng: Machine learning for brain and mental health.
• Willem Diepeveen: Riemannian geometry for inverse problems in cryogenic electron microscopy.
• Hendrik Dirks: Imaging of intracellular flows.
• Sören Dittmer: Inverse problems, clinical time-series data, topology optimisation.
• George Rafael Domenikos: Thermodynamics, statistical physics and cryogenics.
• Derek Driggs: Accelerated optimisation algorithms for machine learning and image processing.
• Jiyang Du (Summer student)
• Matthias Ehrhardt (Post-Doc)
• Maureen van Eijnatten (Visiting scholar)
• Carlos Esteve-Yagüe: Partial differential equations and inverse problems.
• Christian Etmann: Inverse problems, deep learning, medical imaging, adversarial examples.
• Qingnan Fan (Visiting PhD student)
• Simon Foucart: Mathematical data science with a strong approximation theory influence.
• Julian Gilbey: Optical character recognition (OCR) in distorted images, missing data imputation, machine learning, inverse problems.
• Guy Gilboa (Visiting scholar)
• Stefano van Gogh: Leverage data-driven methods to regularise the tomographic reconstruction problem.
• Joana Grah: Mathematical image analysis for cancer research applications.
• Tamara Grossmann: Deep learning, PDE-based image analysis, total variation flow, medieval paper analysis.
• Nadja Gruber: Chan-Vese models for medical image segmentation.
• Rui Guo: Remote sensing application in agriculture disaster.
• Maria Hänel: Optimal placement of cameras.
• Stephan Hilb (Visiting scholar)
• Rob Hocking: Image and video inpainting.
• Olivia Hu (Summer student)
• Chaoyan Huang: Convergence of plug-and-play methods for inverse problems.
• Jiahao Huang: Medical image analysis: object detection, reconstruction, super-resolution, segmentation.
• Kevin Han Huang (Summer student)
• Chris Irving: Building imaging devices: from hardware to software.
• Rihuan Ke: Inverse problems in adaptive optics and post-processing, image processing and numerical linear algebra.
• Hanne Kekkonen (Post-Doc)
• Mohamed Mikhail Kennerly: Domain adaptation for adverse weather conditions and semi-supervised learning.
• Maximilian Kiss: CT reconstruction.
• Ziad Kobeissi: Generating artificial fingerprints.
• Ula Komorowska: PDEs, finite elements, deep learning.
• Yury Korolev: Inverse problems, variational methods, mathematical Imaging, theoretical machine learning.
• Madeleine Kotzagiannidis: Analysis and exploitation of geometric structure in data.
• Rosa Kowalewski: Deep neural networks for image registration.
• Lisa Kreusser: Partial differential equations, data analysis, and mathematical formulations for machine learning.
• Daniel Kreuter: Domain generalisation, iron deficiency detection from blood, federated learning.
• Rien Lagerwerf: TGV-type inpainting for limited-angle tomography.
• Lukas Lang (Post-Doc)
• Jonas Latz: Inverse problems, uncertainty quantification.
• Juheon Lee: Mapping individual trees from airborne multi-sensor imagery.
• Jan Lellman: Machine-learning methods for modelling continuous structural heterogeneity in cryo-EM reconstruction.
• Chao Li: Neuroimaging, multi-omics, computational neuroscience, machine learning & deep learning.
• Ruoteng Li (Visiting PhD student)
• Zhuqing Li (Summer student)
• Jingwei Liang: Non-smooth optimisation, image processing and machine learning.
• Jiulong Liu (Visiting scholar)
• Lihao Liu: Advancing 3D segmentation: deep learning techniques for video and medical imaging.
• Pan Liu (Post-Doc)
• Shuai Liu (Visiting scholar)
• Yaofang Liu: Diffusion modelling, geometric deep learning.
• Sebastian Lunz: Learning regularisation functionals and operator corrections.
• Georg Maierhofer: Learning an optimal sampling pattern for MRI.
• Fangliangzi Meng: Integration of radiomics and genomics, multiomics data analysis and machine learning.
• Subhadip Mukherjee: Machine learning, inverse problems in imaging, optimisation, signal processing.
• Rashmi Murthy: Inverse problems, applied partial differential equations.
• Sebastian Neumayer: Indirect image matching for tomographic inversion under shape priors.
• Emile Okada: Building imaging devices: from hardware to software.
• Ozan Öktem (Visiting scholar)
• Nicolas Papadakis: Computer vision, computational photography, oceanography, medical imaging.
• Kostas Papafitsoros: Higher-order regularity in imaging.
• Evangelos Papoutsellis: First-order gradient regularisation methods for image restoration.
• Simone Parisotto: Mathematics for cultural heritage, inverse problems.
• Hankui Peng: Subspace clustering, constrained clustering, active learning, image recognition, text mining.
• Laurent Pin: Graph-based medical image segmentation.
• Jean Prost: Hierarchical VAEs for image restoration.
• Ziqi Qin: Non-smooth optimisation.
• Lara Raad (Visiting scholar)
• Ethan Redmond (Summer intern)
• Elena Resmerita (Visiting scholar)
• Erlend Skaldehaug Riis: Geometric numerical integration for optimisation.
• Torbjørn Ringholm (Visiting PhD student)
• Christina Runkel: Geometric Deep Learning.
• Malena Sabaté Landman: Inverse Problems, mathematical Imaging, numerical linear algebra, generative models, functional data analysis.
• Simone Saitta: Physics informed denoising and super-resolution of 4D flow MRI.
• Goezde Sarikaya (Intern)
• Ramona Sasse (Visiting PhD student)
• Marie Foged Schmidt (Visiting scholar)
• Timothée Schmoderer: Joint Hybrid Variational Models.
• Ben Schreiber: A Deep Learning Approach to Automated Coeliac Disease Diagnosis.
• Johannes Schwab: Machine-learning methods for modeling continuous structural heterogeneity in cryo-EM reconstruction.
• Philip Sellars: Minimal Labels, Maximum Gain.
• Zhenda Shen: Implicit Neural Representations.
• Ferdia Sherry: Structure-preserving machine learning for inverse problems / Bilevel optimisation.
• Wuhyun Sohn (Summer intern)
• Jan Stanczuk: Topics in Deep Generative Modelling.
• Hong Ye Tan: Machine learning with structural guarantees.
• Junqi Tang: Optimization, deep learning, medical imaging.
• Wei Tang: A Hybrid Model for Vessel Skeleton Extraction.
• Kasia Targonska-Hadzibabic: Mathematics for cultural heritage.
• Sam Thomas (Intern)
• Matthew Thorpe (Post-Doc)
• Rob Tovey: Mathematical challenges in electron tomography.
• Samuel Tull: PhD in the BloodCounts!
• Tuomo Valkonen (Post-Doc)
• Vladimir Vankov: Classification and Standardization of Ancient Pottery by Machine Learning and Geometric Analysis.
• Patricia Vitoria Carrera (Visiting PhD student)
• Verner Vlacic (Intern)
• Marianne de Vriendt: Learning to Classify Medical Images with Minimal Supervision.
• Nils Wachsmuth (Visiting student)
• Shujun Wang (Visiting PhD student)
• Xiaoyu Wang: Machine learning for cell tracking in light microscopy.
• Kaixuan Wei (Visiting scholar)
• Tao Wei (Visiting scholar)
• Jonathan Williams: Analysis of airborne imaging data.
• Tobias Wolf: Inverse problem.
• Yijun Yang: Analysis mammography images for breast cancer classification.
• Gaohang Yu (Visiting scholar)
• Jongmin Yu: Machine learning for brain and mental health.
• Yao Yuan (Visiting scholar)
• Jianchao Zhang: Superpixels segmentation.
• Lipei Zhang: Physics-informed unsupervised learning in healthcare.
• Ping Zhong (Visiting scholar)
• Caroline Zhu: Learning Regularisation for Optical Flow.
• Lei Zhu: Computer vision, image and video processing, medical imaging, and deep learning.
• Jing Zou: Feature Based Image Registration.