Johannes C. Eichstaedt

Assistant Professor (Research) of Psychology
Concentration Advising in:
Academic Appointments
Assistant Professor (Research), Psychology
Member, Bio-X
Member, Wu Tsai Human Performance Alliance
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 what is now a big data psychology consortium, the World Well-Being Project at the University of Pennsylvania.
I use social media (Facebook, Twitter, Reddit, …) to measure the psychological states of large populations and individuals to determine 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 large populations unobtrusively. This is especially relevant for measuring mental health and well-being for populations in places where no traditional measures are available. 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.
Beyond passive monitoring, Large Language Models and related AI systems can screen patients’ mental health, and deliver psychotherapy and well-being interventions. This is the current focus of our lab.
A key emphasis of our work is to use the new generation of data science and AI for good, to benefit well-being and health.
At Stanford, I direct the Computational Psychology and Well-Being lab. In 2011, I co-founded what is now a big data psychology consortium, the World Well-Being Project at the University of Pennsylvania.
I use social media (Facebook, Twitter, Reddit, …) to measure the psychological states of large populations and individuals to determine 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 large populations unobtrusively. This is especially relevant for measuring mental health and well-being for populations in places where no traditional measures are available. 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.
Beyond passive monitoring, Large Language Models and related AI systems can screen patients’ mental health, and deliver psychotherapy and well-being interventions. This is the current focus of our lab.
A key emphasis of our work is to use the new generation of data science and AI for good, to benefit well-being and health.
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)
Degrees / Education
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)
Additional Links