معرفی
Dr. Emmeke Aarts is an Associate Professor in Statistics at Utrecht University's Department of Methodology and Statistics, within the Faculty of Social and Behavioural Sciences. She serves as Director of Education for the department and coordinates several academic programs including the Research Master Methodology and Statistics. Her research focuses on developing novel statistical methods for intensive longitudinal data, particularly multilevel hidden Markov models and real-time prediction algorithms in healthcare. Key areas include personalized latent dynamics and cardiovascular disease monitoring. She has received a 2024 fellowship for her work on depression dynamics in emerging adults. A资深的统计学家 and educator, she supervises PhD students and contributes to interdisciplinary collaborations with medical institutions like UMC Utrecht.
Education: Research Master in Methodology and Statistics (cum laude, Utrecht University) and a PhD in interdisciplinary statistics and neuroscience (VU University Amsterdam). Postdoctoral roles included positions at the Max Planck Institute and TNO.
Research Themes: Applied Data Science, Multilevel Analysis, Machine Learning, and Hidden Markov Models. Her work bridges methodological innovation with practical applications in mental health and cardiology. Current projects include Health-Holland grant-funded collaborations on heart failure prediction and real-time deterioration monitoring.
Teaching: Coordinates courses such as 'Introduction to Multilevel Modelling' and leads summer schools on structural equation modeling. Supervises over 10 graduate students and provides statistical consultation for biomedical and social science projects.
Grants & Awards: 2024 Fellowship for personalized depression dynamics research; Health-Holland grants for heart failure machine learning projects. Extensive record of interdisciplinary funding and academic service roles in education committees.
Labs/Teams: Active in the Utrecht Platform for Applied Data Science and collaborates with UMC Utrecht Cardiology Department. Leads methodological development for multilevel HMM applications in behavioral and biomedical data.
