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
Homeomorphic Image Registration via Conformal-Invariant Hyperelastic
Regularisation
(2023)
Continuous U-Net: Faster, Greater and Noiseless
(2023)
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for
Semi-Supervised Learning
(2022)
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
(2022)
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
(2022)
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semisupervised Classification
– IEEE Trans Neural Netw Learn Syst
(2022)
PP,
1
(DOI: 10.1109/tnnls.2022.3203315)
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,
210391
(DOI: 10.1148/radiol.2021210391)
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