Johannes C. Eichstaedt
Assistant Professor (Research) of Psychology
Ph.D., University of Pennsylvania, Psychology (2017)
M.A., University of Pennsylvania, Psychology (2013)
MAPP, University of Pennsylvania, Positive Psychology (2011)
M.S., University of Chicago, Particle Physics (2010)
B.S. (Hons.), King's College, London, Physics & Philosophy (2009)
Concentration Advising in:
Academic Appointments
Assistant Professor (Research), Psychology
Member, Bio-X
Member, Wu Tsai Human Performance Alliance
Honors & Awards
John Philip Coghlan Fellowship, Stanford (2023-2025)
Rising Star, Association for Psychological Science (2022)
Early Career Researcher Award, International Positive Psychology Association (2021)
Emerging Leader in Science & Society, American Association for the Advancement of Science (AAAS) (2014)
I am a computational social scientist in psychology, an Assistant Professor in Psychology, and the Shriram Faculty Fellow at the Institute for Human-Centered Artificial Intelligence.
At Stanford, I direct the Computational Psychology and Well-Being Lab. In 2011, I co-founded the World Well-Being Project at the University of Pennsylvania, which is now a big data psychology consortium.
How can Large Language Models (LLMs) be deployed for better mental health and well-being? One of the main directions of our lab is to determine the safe and responsible conditions under which LLMs can deliver psychotherapy and well-being interventions.
Over the last decade, we’ve pioneered methods of psychological text analysis. Specifically, we use social media (Facebook, Twitter, Reddit, …) to measure the psychological states of populations and individuals. We use this to understand the thoughts, emotions, and behaviors that drive physical illness (like heart disease), depression, or support psychological well-being.
Such NLP approaches allow us to measure the psychology of populations unobtrusively—without needing to collect survey data. This is particularly helpful in under-resourced settings. The social media-based methods have sufficient spatial and temporal resolution to measure the impact of economic or social disruptions and to inform public policy (e.g., weekly county estimates).
A key emphasis of our work is to use the new generation of LLMs, data science, and AI for good, to benefit the social good, well-being, and health.
At Stanford, I direct the Computational Psychology and Well-Being Lab. In 2011, I co-founded the World Well-Being Project at the University of Pennsylvania, which is now a big data psychology consortium.
How can Large Language Models (LLMs) be deployed for better mental health and well-being? One of the main directions of our lab is to determine the safe and responsible conditions under which LLMs can deliver psychotherapy and well-being interventions.
Over the last decade, we’ve pioneered methods of psychological text analysis. Specifically, we use social media (Facebook, Twitter, Reddit, …) to measure the psychological states of populations and individuals. We use this to understand the thoughts, emotions, and behaviors that drive physical illness (like heart disease), depression, or support psychological well-being.
Such NLP approaches allow us to measure the psychology of populations unobtrusively—without needing to collect survey data. This is particularly helpful in under-resourced settings. The social media-based methods have sufficient spatial and temporal resolution to measure the impact of economic or social disruptions and to inform public policy (e.g., weekly county estimates).
A key emphasis of our work is to use the new generation of LLMs, data science, and AI for good, to benefit the social good, well-being, and health.