My research lies at the intersection of computational mathematics and machine learning for applications to large-scale real world problems.
Keywords: Computational Mathematics 
Publications
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
(2022)
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for
Semi-Supervised Learning
(2022)
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
(2022)
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semisupervised Classification
– IEEE Transactions on Neural Networks and Learning Systems
(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 Transactions on Image Processing
(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)
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)
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