Valencia, Valencian Community, Spain
I build and execute AI-driven health innovation within the European medical technology landscape. Trained as a medical software engineer, with more than a decade of experience in data, AI and software engineering, I operate with technical and scientific rigor. I code, understand system dependencies and translate research into deployable products. At the same time, I love designing partnerships, structure funding pathways, and position innovation within policy and economic frameworks. As founder and CEO of Caona Health, I lead the development of a scientifically grounded, AI-enabled platform for hormonal health. My responsibility spans product architecture, data strategy, regulatory alignment and market implementation across Germany and Europe. Execution is king (queen). Health technology only creates value when it moves from concept to validated implementation. So, my work focuses on building systems that are clinically informed, technologically robust and scalable within European healthcare structures. As founder of Femtech Germany, I remain engaged in advancing women’s health innovation at a structural level. My focus lies on strengthening Germany's and particularly NRW's role within a competitive European health technology ecosystem. I bring together scientific and technical depth with strategic clarity, all while including a collaborative, value-driven leadership style that enables longterm partnerships across sectors. For conversations around AI in healthcare, European health innovation strategy or technology partnerships with real implementation ambition, I am open to connecting.
Doctoral research in collaboration with clinical endocrinology, developing multimodal AI models for the early detection of hormonal dysregulation, particularly during perimenopausal transition phases. Within MENOSTIK "AI-Based Early Detection of Perimenopause", a BMFTR-funded research project, I design and validate machine learning systems that integrate wearable-derived physiological time-series data, voice-based biomarkers, structured symptom documentation, and clinical hormone parameters. The objective is to establish robust digital biomarkers that enable earlier and more objective detection compared to retrospective symptom-based diagnostics. The research combines statistical modeling, multimodal data fusion, explainable AI, and clinical validation to advance preventive, data-driven diagnostics in gynecological endocrinology.
Caona Health develops AI-based diagnostic solutions for hormonal and endocrine disorders, addressing the lack of objective, data-driven tools in female health. Today, many hormone-related conditions are diagnosed late and primarily based on subjective symptom reporting or isolated lab tests. We build machine learning systems that analyze wearable-derived physiological data, cycle tracking information, and patient-reported outcomes to identify patterns associated with menstrual and endocrine dysfunction. Our focus lies in digital biomarker development, time-series modeling, and predictive analytics to transform continuous biometric data into clinically actionable insights. By combining artificial intelligence, medical validation, and digital health infrastructure, we aim to enable earlier detection, improved monitoring, and precision diagnostics in female endocrinology.
As founder of our NGO, I am dedicated to empowering and connecting innovators, researchers, and healthcare professionals within Germany's FemTech industry. Our goal is to bridge gender disparities in healthcare by fostering collaboration, advancing education, and developing innovative, women-centered solutions. My vision is to establish Germany as a leading hub for FemTech innovation, where women have access to personalized and effective healthcare solutions. I am committed to creating a society where gender equality in health is achieved, and women's health innovations are recognized as essential to public health progress.
As CTO of Datalytics, I led the technical strategy and execution of data- and AI-driven solutions across multiple industry domains. I was responsible for overall system architecture, infrastructure design, and the development of scalable data pipelines that transformed complex datasets into actionable intelligence. My role bridged engineering, product, and business strategy, overseeing the full lifecycle of data science systems from problem definition and model development to production deployment and operational scaling. I built and led interdisciplinary teams, established robust technical standards, and ensured alignment between machine learning innovation, secure infrastructure, and client-focused delivery.