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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.

Keywords: Computational Mathematics \bigstar Inverse problems \bigstar  Computer Vision \bigstarMedical Image Analysis  \bigstarRobotics \bigstarMachine Learning.



Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction.
AI Aviles-Rivero, G Williams, MJ Graves, C-B Schonlieb
– CoRR
Towards Retrieving Force Feedback in Robotic-Assisted Surgery: A Supervised Neuro-Recurrent-Vision Approach
AI Aviles, SM Alsaleh, JK Hahn, A Casals
– IEEE Trans Haptics
Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach
AI Aviles, T Widlak, A Casals, MM Nillesen, H Ammari
– Physics in medicine and biology
Dynamic Spectral Residual Superpixels
J Zhang, AI Aviles-Rivero, D Heydecker, X Zhuang, R Chan, C-B Schönlieb
Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction
J Liu, AI Aviles-Rivero, H Ji, C-B Schönlieb
Dim the Lights! -- Low-Rank Prior Temporal Data for Specular-Free Video Recovery
SM Alsaleh, AI Aviles-Rivero, N Debroux, JK Hahn
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
K Wei, A Aviles-Rivero, J Liang, Y Fu, C-B Schnlieb, H Huang
The GraphNet Zoo: A Plug-and-Play Framework for Deep Semi-Supervised Classification
MD Vriendt, P Sellars, AI Aviles-Rivero
Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-Resolution
V Corona, AI Aviles-Rivero, N Debroux, CL Guyader, C-B Schönlieb
When Labelled Data Hurts: Deep Semi-Supervised Classification with the Graph 1-Laplacian
AI Aviles-Rivero, N Papadakis, R Li, SM Alsaleh, RT Tan, C-B Schonlieb
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