![](http://www.damtp.cam.ac.uk/research/cia/files/styles/portrait/public/portraits/ai323.png?itok=2cLWFed_)
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
Tuning-free plug-and-play proximal algorithm for inverse imaging problems
– 37th International Conference on Machine Learning, ICML 2020
(2020)
PartF168147-14,
10089
Dim the Lights! -- Low-Rank Prior Temporal Data for Specular-Free Video
Recovery
(2019)
GraphX $^\mathbf{\small NET } -$ N E T - Chest X-Ray Classification Under Extreme Minimal Supervision
– Lecture Notes in Computer Science
(2019)
11769,
504
(doi: 10.1007/978-3-030-32226-7_56)
RainFlow: Optical Flow Under Rain Streaks and Rain Veiling Effect
– Proceedings of the IEEE International Conference on Computer Vision
(2019)
2019-October,
7303
(doi: 10.1109/iccv.2019.00740)
Variational Multi-Task MRI Reconstruction: Joint Reconstruction,
Registration and Super-Resolution
(2019)
Semi-Supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
– International Geoscience and Remote Sensing Symposium (IGARSS)
(2019)
00,
592
(doi: 10.1109/igarss.2019.8898189)
Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All?-A Tailored Approach to Single Image Reflection Removal.
– IEEE Trans Image Process
(2019)
28,
6185
(doi: 10.1109/tip.2019.2923559)
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised
Classification with the 1-Laplacian Graph Energy
(2019)
ReTouchImg: Fusioning from-local-to-global context detection and graph data structures for fully-automatic specular reflection removal for endoscopic images.
– Comput Med Imaging Graph
(2019)
73,
39
Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
– SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION, SSVM 2019
(2019)
11603,
263
(doi: 10.1007/978-3-030-22368-7_21)
- <
- 4 of 5