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: 
Publications
GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays
– Pattern Recognition
(2021)
122,
108274
(doi: 10.1016/j.patcog.2021.108274)
Learning optical flow for fast MRI reconstruction
– Inverse Problems
(2021)
37,
095007
(doi: 10.1088/1361-6420/ac164a)
Delving Into Deep Walkers: A Convergence Analysis of Random-Walk-Based
Vertex Embeddings
(2021)
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep
Semi-Supervised Classification
– IEEE Transactions on Neural Networks and Learning Systems 2022
(2021)
Dynamic spectral residual superpixels
– Pattern Recognition
(2021)
112,
107705
(doi: 10.1016/j.patcog.2020.107705)
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 LNCS,
187
(doi: 10.1007/978-3-030-60365-6_18)
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