Carlos Cano Espinosa is an Assistant Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante. He holds a PhD in Computer Science with international distinction and a Master’s degree in Automation and Robotics, both from the same institution. His research focuses on medical image analysis using artificial intelligence, deep neural networks, and machine learning, with applications in computer-aided diagnosis, biomarker quantification, and digital health.

He has conducted research at leading institutions such as Brigham and Women’s Hospital (Harvard Medical School), where he contributed to the development of AI-based models for airway tree segmentation, mucus plug detection, and the quantification of brown adipose tissue activation from PET/CT imaging. He also participated in studies on the non-invasive assessment of coronary artery disease using computed tomography. Subsequently, he completed a postdoctoral fellowship funded by the María Zambrano program at the Polytechnic University of Cartagena, focused on deep learning approaches for ocular disease diagnosis. In addition, he has collaborated on projects funded by the Valencian Innovation Agency involving radiological image analysis for respiratory diseases such as COVID-19.

He is the author of scientific articles published in high-impact journals such as IEEE Transactions on Medical Imaging, Medical Image Analysis, and NPJ Digital Medicine. His academic career combines strong research experience with active teaching responsibilities, delivering core courses in programming and data structures in undergraduate computer engineering programs.