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Department of Pure Mathematics and Mathematical Statistics


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My research lies  at the intersection of computational mathematics and machine learning for applications to large-scale real world problems. My central research is to develop new data-driven algorithmic techniques that allow computers to gain high-level understanding from vast amounts of data, this, with the aim of aiding the decisions of users. These methods are based on mathematical modelling and machine learning methods.

Keywords: \bigstarApplied Mathematics \bigstar Computational Mathematics \bigstar Inverse problems \bigstar  Image Analysis  \bigstar Graph Learning \bigstar Machine Learning.



Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
J Zou, N Debroux, L Liu, J Qin, C-B Schönlieb, AI Aviles-Rivero
Continuous U-Net: Faster, Greater and Noiseless
C-W Cheng, C Runkel, L Liu, RH Chan, C-B Schönlieb, AI Aviles-Rivero
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Z Deng, R Ke, C-B Schonlieb, AI Aviles-Rivero
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Z Deng, Y Chen, L Liu, S Wang, R Ke, C-B Schonlieb, AI Aviles-Rivero
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
L Liu, J Prost, L Zhu, N Papadakis, P Liò, C-B Schönlieb, AI Aviles-Rivero
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semisupervised Classification
P Sellars, AI Aviles-Rivero, C-B Schonlieb
– IEEE Transactions on Neural Networks and Learning Systems
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
Y Cheng, L Liu, S Wang, Y Jin, C-B Schönlieb, AI Aviles-Rivero
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
AI Aviles-Rivero, C Runkel, N Papadakis, Z Kourtzi, C-B Schönlieb
– MICCAI 2022
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
R Ke, AI Aviles-Rivero, S Pandey, S Reddy, C-B Schonlieb
– IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis
SE Hickman, R Woitek, EPV Le, YR Im, C Mouritsen Luxhøj, AI Aviles-Rivero, GC Baxter, JW MacKay, FJ Gilbert
– Radiology
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