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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.



HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
A Bryutkin, J Huang, Z Deng, G Yang, C-B Schönlieb, A Aviles-Rivero
Single-Shot Plug-and-Play Methods for Inverse Problems
Y Cheng, L Zhang, Z Shen, S Wang, L Yu, RH Chan, C-B Schönlieb, AI Aviles-Rivero
TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations
Z Shen, Y Cheng, RH Chan, P Liò, C-B Schönlieb, AI Aviles-Rivero
Contrastive Registration for Unsupervised Medical Image Segmentation
L Liu, AI Aviles-Rivero, C-B Schonlieb
– IEEE Trans Neural Netw Learn Syst
Traffic Video Object Detection using Motion Prior
L Liu, Y Cheng, D Chen, J He, P Liò, C-B Schönlieb, AI Aviles-Rivero
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
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