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
GraphX $^\mathbf{\small NET } -$ N E T - Chest X-Ray Classification Under Extreme Minimal Supervision
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(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)
00,
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
– 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
(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
– Computerized Medical Imaging and Graphics
(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 LNCS,
263
(doi: 10.1007/978-3-030-22368-7_21)
Sensory Substitution for Force Feedback Recovery: A Perception Experimental Study
– ACM Transactions on Applied Perception
(2018)
15,
1
(doi: 10.1145/3176642)
Sliding to predict: vision-based beating heart motion estimation by modeling temporal interactions.
– International journal of computer assisted radiology and surgery
(2018)
13,
353
(doi: 10.1007/s11548-018-1702-1)
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