Profile Magdalena Żebrowska

Magdalena Żebrowska

Magdalena Żebrowska holds an MSc and a PhD in Mathematics from the University of Wrocław, Poland. Her doctoral research focused on Bayesian statistics and the performance of regression methods for identifying genes associated with quantitative traits in experimental crosses. Following her doctorate, she moved to Vienna to pursue postdoctoral research in medical statistics at the Medical University of Vienna. At the Center for Medical Statistics, Informatics and Intelligent Systems (CEMSIIS), her work focused on statistical methodology and adaptive designs for clinical trials.

In November 2020, she joined the Department of Epidemiology, where she has continued to develop her research at the interface of statistical methodology, epidemiology, genetic epidemiology, and population health. 

Her international experience includes a postdoctoral appointment at the Channing Division of Network Medicine, Harvard Medical School, Boston. Since 2023, she has also collaborated with the Saxena Lab at the Broad Institute of MIT and Harvard, strengthening her work at the interface of population epidemiology, genomics, and circadian health.

Her work investigates genetic, lifestyle, environmental, and social determinants of health, with particular emphasis on circadian rhythms, sleep, shift work, and their links with cardiometabolic and mental health outcomes. She applies advanced statistical and genetic epidemiological methods to longitudinal and high-dimensional data, including analyses aimed at identifying potentially causal relationships and sources of interindividual variation in disease risk.

Magdalena contributes to several major national and international research initiatives. Within the Horizon Europe BETTER4U consortium, she conducts epidemiological and genetic analyses using Harvard prospective cohort data, including the Health Professionals Follow Up Study and Nurses’ Health Studies, to investigate determinants of obesity and related health outcomes. In the CLOCKrisk project, she performs statistical and genetic analyses to study the health consequences of circadian disruption and individual susceptibility to sleep- and shift-work-related exposures. 

Her research is characterized by interdisciplinary collaboration and the integration of statistical methodology with clinically and public-health-relevant questions.