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
Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2019)
11603 LNCS,
263
(doi: 10.1007/978-3-030-22368-7_21)
Sensory Substitution for Force Feedback Recovery
– 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)
Peekaboo-where are the objects? Structure adjusting superpixels
– 2018 25th IEEE International Conference on Image Processing (ICIP)
(2018)
00,
3693
(doi: 10.1109/icip.2018.8451822)
Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach.
– Phys Med Biol
(2017)
62,
4831
(doi: 10.1088/1361-6560/aa6914)
Towards Retrieving Force Feedback in Robotic-Assisted Surgery: A Supervised Neuro-Recurrent-Vision Approach
– IEEE Transactions on Haptics
(2016)
10,
431
(doi: 10.1109/toh.2016.2640289)
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