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peopleFaculty,Faculty Fellow

Johannes Eichstaedt

Ram and Vijay Shriram HAI Faculty Fellow, Assistant Professor (Research) of Psychology

External Bio

Johannes C. Eichstaedt is a computational social scientist. He is jointly appointed as the Ram and Vijay Shriram HAI Faculty Fellow, and an Assistant Professor (Research) in the Psychology Department. Johannes obtained his Ph.D. in Psychology and has been a Senior Research Associate at the Positive Psychology Center at the University of Pennsylvania. In 2011 he co-founded and led the World Well-Being Project, bringing together computer scientists and psychologists, which has since attracted $3.9m in funding. Before joining the social sciences, Johannes did research in particle physics with an M.S. from the University of Chicago. In 2014, he was elected an Emerging Leader in Science & Society by the American Association for Advancement of Science (AAAS). In his non-academic time he practices Tai Chi and goes on long-distance hikes.

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Latest Related to Johannes Eichstaedt

media mention
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Chatbots, Like the Rest of Us, Just Want to Be Loved

Wired
Natural Language ProcessingMachine LearningGenerative AIFoundation ModelsMar 05

A study led by Stanford HAI Faculty Fellow Johannes Eichstaedt reveals that large language models adapt their behavior to appear more likable when they are being studied, mirroring human tendencies to present favorably.

policy brief

Toward Responsible Development and Evaluation of LLMs in Psychotherapy

Elizabeth C. Stade, Shannon Wiltsey Stirman, Lyle Ungar, Cody L. Boland, H. Andrew Schwartz, David B. Yaden, João Sedoc, Robert J. DeRubeis, Robb Willer, Jane P. Kim, Johannes Eichstaedt
HealthcareQuick ReadJun 13

This brief reviews the current landscape of LLMs developed for psychotherapy and proposes a framework for evaluating the readiness of these AI tools for clinical deployment.

Research
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Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency

Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt
May 31

Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency

All Related

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency
Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt
Mar 16, 2022
Research
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Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt
Mar 16, 2022

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

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Research
AI-Enabled Depression Prediction Using Social Media
Johannes Eichstaedt
Quick ReadFeb 01, 2021
policy brief
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Natural language processing for mental health monitoring is an emerging use of AI poised to disrupt the landscape of the health care industry.

AI-Enabled Depression Prediction Using Social Media

Johannes Eichstaedt
Quick ReadFeb 01, 2021

Natural language processing for mental health monitoring is an emerging use of AI poised to disrupt the landscape of the health care industry.

Healthcare
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policy brief
How Trump Won the Unhappiness Vote
Johannes Eichstaedt
Sep 18, 2020
news

New research shows our mental well-being drives our decisions at the ballot box. 

How Trump Won the Unhappiness Vote

Johannes Eichstaedt
Sep 18, 2020

New research shows our mental well-being drives our decisions at the ballot box. 

Healthcare
news