Sajia Darwish is a master’s student in Epidemiology and Biostatistics at the UC Berkeley School of Public Health. Her research interests center on using data analytics, statistical methods, and machine learning to understand and solve problems in health. As a graduate student researcher at CTML, Sajia works under the supervision of Alan Hubbard to harmonize data and apply statistical methods and machine learning techniques to analyze the relationships between maternal milk composition and child growth. She is also a special project researcher at UC Berkeley’s D-Lab and the American Medical Association, and a quantitative intern at Mujeres Unidas y Activas. Previously, Sajia has worked at the Risk Resilience Research Lab, the Stanford Center for Population Health Sciences, and Relief International. Sajia holds a B.A. from Mount Holyoke College and an M.A. from Stanford University.
Data Analytics; Statistical Methods; Machine Learning