Natasa is a third year doctoral candidate in Biobehavioral Science. She holds BSN and MSN degrees and is currently a trainee in sleep science, cognitive aging, and women’s brain aging. Her training supports an emerging expertise in computational biology and neurocomputational methods in large cohort data analysis. Natasa’s research interests center on using machine learning methods to understand how the perimenopause-to-menopause transition, modulates neuroendocrine function, sleep architecture, cognition, and biomarkers relevant to brain aging and neurodegenerative disease.