Jaime S. Cardoso
INESC TEC · Faculty of Engineering, University of Porto
Faculty of Engineering, University of Porto
Porto, Portugal
I am a Full Professor at the Department of Electrical and Computer Engineering (DEEC), Faculty of Engineering of the University of Porto (FEUP), Portugal. I am also a researcher at INESC TEC (Centre for Telecommunications and Multimedia).
My research lies at the intersection of artificial intelligence, computer vision, medical imaging, and machine learning, with a particular focus on developing trustworthy AI systems for healthcare. My interests include explainable AI, uncertainty quantification, robust machine learning, multimodal learning, and clinical decision support systems.
My goal is to develop AI methodologies that are not only accurate but also transparent, reliable, and clinically meaningful, enabling their safe integration into real-world healthcare workflows. I am particularly interested in bridging methodological advances in machine learning with impactful applications in medical imaging.
At INESC TEC, I co-founded:
- the Breast Research Group
- the Visual Computing and Machine Intelligence (VCMI) Group
I have supervised numerous MSc and PhD students and regularly collaborate with academic institutions, hospitals, and industrial partners on national and international research projects.
Prospective Students
I welcome inquiries from prospective PhD students and postdoctoral researchers interested in artificial intelligence, computer vision, medical imaging, trustworthy machine learning, explainable AI, and healthcare applications.
Learn here why Portugal is an excellent destination for advanced studies and research.
Academic Profiles
Recognition
selected publications
- Information bottleneck for attribution applied to object detectionIEEE Transactions on Artificial Intelligence (TAI), 2026
- CBVLM: Training-free Explainable Concept-based Large Vision Language Models for Medical Image ClassificationComputers in Biology and Medicine (Elsevier), 2025
- Unimodal Distributions for Ordinal RegressionIEEE Transactions on Artificial Intelligence (TAI), 2025
- Anonymizing medical case-based explanations through disentanglementMedical Image Analysis (Elsevier), 2024
- Weather and Meteorological Optical Range Classification for Autonomous DrivingIEEE Transactions on Intelligent Vehicles, 2024
- Machine Learning Interpretability: A Survey on Methods and MetricsElectronics (section: Artificial Intelligence), 2019
