Dr. Sebastian Mildiner Moraga is an Assistant Professor and Senior Statistician in the Department of Data Science and Biostatistics at the Julius Center, University Medical Center Utrecht, the Netherlands. His work focuses on Real-World Evidence, causal inference, and advanced statistical modeling, with a particular interest in generating robust evidence from observational healthcare data to support clinical, regulatory, and policy decision-making.

Sebastian obtained his PhD in Methods and Statistics from Utrecht University, where he developed novel Bayesian multilevel hidden Markov models for the analysis of complex longitudinal data. During his doctoral research, he collaborated with researchers in psychiatry, neuroscience, and economic geography, leading methodological projects and developing open-source statistical software for the analysis of intensive time-series data.

At the Julius Center, Santiago contributes to international real-world evidence studies in collaboration with pharmaceutical companies, regulators, and research partners across Europe. His work includes post-authorization safety and effectiveness studies of vaccines and medicines, target trial emulation, federated analytics, and methodological research on causal inference using observational data. He has worked on projects commissioned by the European Medicines Agency (EMA) and industry partners, supporting the generation of regulatory-grade evidence from large healthcare databases.

In addition to his work in healthcare, Sebastian has contributed to research in machine learning, computational social science, and personalized prediction. He was a research intern at Snap Inc.'s Computational Social Science Lab (Santa Monica, US), where he worked on user personalization and representation learning using large-scale behavioral data.

Sebastian has authored methodological and applied publications in statistics and health research, he has regularly presented his work at international conferences, and collaborates with multidisciplinary teams spanning epidemiology, medicine, computer science, and statistics. He has also been actively involved in teaching and mentoring, contributing to courses in data science, causal inference, and statistical methodology for undergraduate and graduate students.