Master's Degree in Artificial Intelligence

PhD Candidate in Intelligent Systems for Health and AI Engineer bridging Computer Science, Business, and Artificial Intelligence. I design clinically grounded models across medical imaging, biomedical NLP, and Computer Vision. I earned an M.Sc. in AI at Universidad Europea, awarded three “Matrícula de Honor” (highest honors) in Neural Networks, NLP, and AI in Business. RESEARCH FOCUS Active member of the Research Group on Intelligent Technological Applications and Sustainability (GIATIS), where I contribute to research in medical AI applications. My current research centers on "Detection and classification of lesions in mammographic thermal images through deep learning algorithms," developing innovative solutions using Convolutional Neural Networks and Visual Transformer (ViT) architectures along with synthetic data to enhance diagnostic precision in resource-limited clinical settings. CURRENT PROJECTS 1. Benchmark for Multimodal Agentic System for variant identification in rare deseases. 2. MultimodalBioQA — Agentic, explainable Vision Language Model +NLP framework for biomedical Q&A. 3. Agentic MCS — LangGraph-based multilingual & multimodal biomedical summarization. RESEARCH INTERESTS My research interests lie at the intersection of Multimodal AI, Natural Language Processing, Computer Vision and Deep Learning applied to Health. With over 15 years of experience in biomedical multilingual NLP systems, I specialize in developing AI solutions that bridge language and modal barriers in healthcare applications. My work encompasses: • AI Agents: Development of autonomous systems for healthcare process automation. • Multimodal AI Systems: Vision–language models for clinical reasoning, triage support, and case-based explanations. • Deep Learning Architectures: Deep learning for medical image analysis and diagnostic support. • Explainability & AI Safety: Post-hoc and intrinsic XAI, calibrated prediction intervals, robustness, and governance for clinical decision support. • Quantum-Inspired Methods: Tensor networks (MPS/TT) for embeddings, retrieval, and TT-adapters as parameter-efficient alternatives to LoRA. PROFESSIONAL IMPACT As AI Engineering Manager at YeY Digital, I have successfully translated academic research into real-world applications, leading cross-functional teams in developing AI solutions for global organizations. My academic contributions include university lecturing positions and leadership roles in professional organizations. RESEARCH PHILOSOPHY Driven by a passion for languages and technology that began with early academic scholarships, my research focuses on creating AI systems that not only advance technical capabilities but also address real societal needs, particularly in healthcare accessibility and cross-cultural communication. I am committed to developing explainable AI solutions that can be trusted and adopted in critical domains like medical diagnosis and treatment.