As a dedicated researcher and data scientist specializing in the intersection of healthcare and artificial intelligence, my work focuses on developing advanced predictive models that improve patient outcomes. With extensive experience in machine learning, meta-learning, and real-time predictive analytics, I have contributed to cutting-edge projects aimed at early detection of critical conditions like atrial fibrillation. My academic journey, supported by robust collaborations with leading institutions like Amsterdam UMC, has allowed me to combine clinical insights with technical innovation, ensuring that my research is not only impactful but also directly applicable in improving healthcare systems.

Throughout my career, I have published widely in peer-reviewed journals and presented at international conferences, where my work has been recognized for its potential to transform patient care. My passion lies in leveraging big data and artificial intelligence to create scalable, efficient solutions in the medical field. I continue to explore how machine learning algorithms, particularly machine learning approaches, can be applied to complex, real-world medical datasets to advance both clinical research and the delivery of healthcare services.

Focus area: Online monitoring, Data Science, Meta-learning,Medical Time-Series, Mixed-effect Models, Applied Statistics.