Hello and welcome!
My name is Martje Rave, and I am a data scientist and statistician with a PhD in Statistics from LMU Munich. I specialize in regression modelling and the analysis of complex health data, combining methodological research with real-world applications.
I am currently working as a Data Scientist at Dansk Gigthospital, where I apply statistical and machine learning methods to healthcare data.
My research focuses on exploratory and predictive modelling, with a particular interest in areal and health data analysis. I work with advanced statistical methodologies, including generalized additive models and Bayesian inference, to derive actionable insights from incomplete, high-dimensional, and temporally structured data.
During my PhD, I focused on modelling ICU occupancy and patient flow dynamics during the COVID-19 pandemic. I contributed to public health reporting and supported the Bavarian Health Authority (LGL) in forecasting ICU demand across Bavaria.
More recently, I developed methods to impute missing ICU admission data and reconstruct unobserved patient pathways, enabling deeper insights into ICU length of stay and disease severity over time.
In addition to my research, I have extensive teaching experience in regression methodology, Bayesian modelling, and spatial statistics at both Bachelor’s and Master’s level, mainly using R. I have also guided master theses and led seminars on Open Science and advanced statistical modelling.
If you would like to connect, feel free to reach out via email or on LinkedIn.