Optimize and deploy AI pipelines on embedded systems to ensure high-performance inference while minimizing resource consumption. Conduct Computer Vision and Machine Learning research. Perform full-cycle software development, including designing and implementing scalable AI solutions from prototype to production.

Advanced research on Multiple Sclerosis detection with a focus on developing and implementing deep learning models using 2D and 3D OCT and OCTA images from the macula and optic nerve regions of the retina. Managed the full data pipeline and designed computer vision models for vessel segmentation, optic nerve head location detection, fovea segmentation in OCTA, and layer segmentation in OCT. Implemented algorithms to represent vasculature as graphs and analyze them using Graph Neural Networks, along with multiview and multimodal computer vision algorithms for MS detection.

Initiated research on Multiple Sclerosis detection using OCT and OCTA data from the macula and optic nerve regions of the retina. Developed comprehensive machine learning pipelines encompassing data acquisition, preprocessing, model training, evaluation, and deployment into medical tools. Created foundational algorithms for layer segmentation in OCT and 2D OCTA image reconstruction from 3D volumes.Links to related published works:1. https://www.die.upm.es/im/papers/CASEIB2023_GarciaMesa.pdf2. https://onlinelibrary.wiley.com/doi/abs/10.1111/aos.16095

Developed CNN-based computer vision models for melanoma detection and nevus segmentation, led the development of an Android application integrating these models, and collaborated on backend development by building API and SQL database components. Coordinated and managed the software development team, and participated in public competitions, gaining valuable experience in rapid adaptation and innovation within a fast-paced startup environment.