Hey! I'm an artificial intelligence researcher in Spain, interested in machine learning, computer vision, trying to materialize the networks with some projects!
I completed a Bachelor's Degree in Informatics Engineering and later a Master's Degree in Artificial Intelligence, Pattern Recognition and Digital Imaging at the Polytechnic University of Valencia (Spain). Now I am doing my Ph.D. in Computer Vision relating to domain adaptation and transfer learning under medical image segmentation constraints. I like to compete in Kaggle competitions where I try to win other scientists from all over the world, where little by little I am learning, but I often delve into other hobbies such as music and sports.
I’m currently conducting research into various areas of machine learning, computer vision and artificial intelligence. You can see all my publications here.
Working in the area of Computer Vision in a project for PAVASAL S.A. in the automatic recognition through images of the different damages of the asphalt.
Deep-Learning and HPC to Boost Biomedical Applications for Health at the European project "European Distributed Deep Learning Library". https://deephealth-project.eu/.
Research in state of the art convolutional neural topologies at PRHLT Research Centre.
Studying techniques of transfer learning and domain adaptation under medical imaging constraints.
Master's Degree in Artificial Intelligence, Pattern Recognition and Digital Imaging.
Bachelor’s Degree in Informatics Engineering with a mention in computation.
Asphalt damage detection and segmentation through Convolutional Neural Networks.
Contribute to the effort of building generalizable models that can be applied consistently across clinical centers.
This challenge hopes to improve on approaches to Bengali recognition.
Classify dermoscopic images among nine different diagnostic categories.
Can you identify a whale by its tail?. Challenged to build an algorithm to identify individual whales in images.
"Quick, Draw!" is a game that prompts users to draw an image depicting a certain category, such as ”banana,” “table,” etc. The game generated more than 1B drawings, of which a subset was publicly released as the basis for this competition’s training set. That subset contains 50M drawings encompassing 340 label categories. I had to build a recognizer that can effectively learn from this noisy data and perform well on a manually-labeled test set from a different distribution.