Introduction

Dr. Marcos Matabuena is an Assistant Professor at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He brings over eight years of experience spanning machine learning, biostatistics (theory and methods), biomedical applications, and software development. His research philosophy is guided by a simple principle: trustworthy healthcare analytics require both rigorous foundations and practical deployment. Accordingly, he builds statistically principled models and efficient algorithms designed to operate in real-world digital health workflows and deliver measurable clinical impact.

His work advances statistical AI—particularly functional data analysis, survival analysis, and conformal prediction—motivated by a central challenge in digital health: high-frequency wearable and clinical data are rich, but noisy, heterogeneous, and collected in free-living conditions and under real-world constraints. He develops continuous-time modeling frameworks and uncertainty-aware methods that make modern statistical tools actionable for healthcare research and clinical practice.

Dr. Matabuena has made pioneering contributions to individualized digital health trajectories, including glucodensity, a functional representation of wearable glucose data. He also leads the development of methods for uncertainty quantification in digital health—enabling reliable inference and decision support under real-world variability—as well as methods for analyzing random objects in metric spaces. These contributions have demonstrated impact across diabetes, aging, ALS, and cancer—major public health challenges worldwide.

Looking ahead, his agenda is to advance reliable statistical AI for digital health and to develop mathematical tools that integrate wearable signals with genetic data to enable precision medicine.

If you have ideas or problems where rigorous statistical methodology could make a meaningful difference in a clinical or biomedical setting, feel free to get in touch. Marcos is open to collaborating on high-impact questions that can make a difference and help define the digital future of healthcare.