Merve Cerit is a computer scientist and engineer, and a PhD candidate at Stanford University, whose research sits at the intersection of digital behavior, wellbeing, and AI.
She builds computational frameworks and measures to study how people develop alongside AI systems, drawing on intensive longitudinal data: dense, moment-by-moment records of digital experience captured within individuals over time. Her work spans the Human Screenome Project (https://screenomics.stanford.edu), where she develops methods for measuring digital experience at the scale of seconds and its impact on mental health, and empirical studies of human-AI relationships, including large-scale research on people in sustained interactions with AI companions. She developed the Media Content Atlas and its longitudinal extension, open frameworks that allow researchers to study complex media and AI experiences at scale. Before her doctorate she worked on commercial applications of machine learning at Microsoft and co-founded an NSF-backed education technology startup. She is a Fulbright Scholar, a Stanford Interdisciplinary Graduate Fellow, and an Emerson Consequential Scholar. At the Berkman Klein Center, she examines sustained human-AI interaction through longitudinal behavioral data, and its implications for how people think, learn, and relate, and for how AI systems should be designed and governed.