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
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet
Recognition through the Lens of Robustness
(2022)
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer
Classification
– MICCAI 2022
(2022)
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
– IEEE Trans Image Process
(2022)
31,
1805
(doi: 10.1109/TIP.2022.3144036)
Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.
– Radiology
(2021)
302,
88
(doi: 10.1148/radiol.2021210391)
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)
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