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: Applied Mathematics Computational Mathematics Inverse problems Image Analysis Graph Learning Machine Learning.
Publications
Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution.
– Medical Image Analysis
(2020)
68,
101941
(doi: 10.1016/j.media.2020.101941)
Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction
– Med Image Anal
(2020)
68,
101930
(doi: 10.1016/j.media.2020.101930)
Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction.
– CoRR
(2020)
abs/1810.10828,
101933
(doi: 10.1016/j.media.2020.101933)
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2020)
12443,
187
(doi: 10.1007/978-3-030-60365-6_18)
Controllable Image Processing via Adaptive FilterBank Pyramid
– IEEE Transactions on Image Processing
(2020)
29,
8043
(doi: 10.1109/tip.2020.3009844)
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
– IEEE Transactions on Geoscience and Remote Sensing
(2020)
58,
4180
(doi: 10.1109/TGRS.2019.2961599)
Tuning-free plug-and-play proximal algorithm for inverse imaging problems
– 37th International Conference on Machine Learning, ICML 2020
(2020)
PartF168147-14,
10089
Dim the Lights! -- Low-Rank Prior Temporal Data for Specular-Free Video
Recovery
(2019)
GraphX $^\mathbf{\small NET } -$ N E T - Chest X-Ray Classification Under Extreme Minimal Supervision
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2019)
11769,
504
(doi: 10.1007/978-3-030-32226-7_56)
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