Research
For as long as I can remember, I have always been fascinated by technology and driven by an eagerness to discover new things through curiosity. This passion stems from my father, whose role in shaping my research career has been vital. He introduced me to technology when I was a young child, and together with my mother, consistently encouraged me to pursue a career in science and engineering.
I spent my early days playing with Legos and memorizing the Star Wars saga. When I was old enough, I received my first Lego Mindstorms set, which was truly a game changer. Through it, I learned to program basic instructions, read sensor data, and debug my numerous mistakes. Most importantly, it sparked a fundamental question that continues to drive my research today:
How do machines interact with our world?
I'm currently enrolled in a Bioinformatics B.Sc. at the University of Málaga. Thanks to opportunities provided by many brilliant professors, I have been fortunate to conduct research in Computer Vision for Biomedical Applications since my third year as an undergraduate, while spending much of my free time exploring robotics, genomic analysis, and mathematics.
Computational Intelligence and Image Analysis Group
I first encountered Artificial Intelligence during the fifth semester of my Bioinformatics B.Sc, while being enrolled in the course "Intelligent Systems". There, I met Ezequiel López-Rubio, PhD, who recognized my enthusiasm for AI and agreed to mentor me in the field. Under his guidance, I began developing projects that gradually strengthened my deep learning-related knowledge, with the ultimate goal of preparing me for my bachelor's thesis research project.
At the end of my third year, Ezequiel and Esteban-José Palomo Ferrer, PhD offered me a Research Assistant position to work on Deep Learning approaches for detecting and characterizing stenosis lesions in invasive coronary angiography videos. This position, affiliated with the Biomedical Research Institute of Málaga (IBIMA), provided me with invaluable hands-on research experience in medical computer vision, where I continue to work today.
Publications during my stay
M. Pascual-González, A. Jiménez-Partinen, E. J. Palomo, E. López-Rubio, and A. Ortega-Gómez, "Hyperparameter optimization of YOLO models for invasive coronary angiography lesion detection and assessment," Computers in Biology and Medicine, vol. 196, p. 110697, 2025, doi: 10.1016/j.compbiomed.2025.110697.
Personal Comments
My first scientific publication. Leaving the statistical analysis aside, the code provides an easy tool that is able to integrate the hyperparameter optimization of any YOLO variant using the Optuna framework. You only have to define your trainer at
optimization/engine/trainers. The scriptoptimization/engine/hpo.pywill catch your trainer class and optimize it with the configuration you defined (see example atoptimization/cfg/files/picasso/yaml), where you must write your trainer class in themodel_sourceentry.