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
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semisupervised Classification.
– IEEE Trans Neural Netw Learn Syst
(2024)
35,
5306
(doi: 10.1109/tnnls.2022.3203315)
HAMLET: Graph Transformer Neural Operator for Partial Differential
Equations
(2024)
Contrastive Registration for Unsupervised Medical Image Segmentation.
– IEEE transactions on neural networks and learning systems
(2023)
PP,
1
(doi: 10.1109/tnnls.2023.3332003)
Learning Homeomorphic Image Registration via Conformal-Invariant
Hyperelastic Regularisation
(2023)
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for
Semi-Supervised Learning
(2022)
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