Institute for Human-Centered Artificial Intelligence (HAI)
Showing 1-10 of 15 Results
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Jef Caers
Professor of Earth and Planetary Sciences and, by courtesy, of Geophysics
On Leave from 09/01/2025 To 06/30/2026Current Research and Scholarly InterestsMy research focuses on assuring 100% renewable energy through development of geothermal energy and critical mineral supply, developing approaches from data acquisition to decision making under uncertainty and risk assessment.
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Danton Char
Associate Professor of Anesthesiology, Perioperative and Pain Medicine (Pediatric)
Current Research and Scholarly InterestsDr. Char's research is focused on identifying and addressing ethical concerns associated with the implementation of next generation technologies like whole genome sequencing and its attendant technologies like machine learning to bedside clinical care.
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Akshay Chaudhari
Associate Professor (Research) of Radiology (Integrative Biomedical Imaging Informatics at Stanford) and of Biomedical Data Science
Current Research and Scholarly InterestsDr. Chaudhari is interested in the application of artificial intelligence techniques to all aspects of medical imaging, including automated schedule and reading prioritization, image reconstruction, quantitative analysis, and prediction of patient outcomes. His interests focus on the development and evaluation new self-supervised and representation learning techniques for multi-modal deep learning in healthcare using vision, language, and medical records data
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Jonathan H. Chen, MD, PhD
Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsInformatics solutions ares the only credible approach to systematically address challenges of escalating complexity in healthcare. Tapping into real-world clinical data streams like electronic medical records will reveal the community's latent knowledge in a reproducible form. Delivering this back as clinical decision support will uniquely close the loop on a continuously learning health system.
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Yejin Choi
Dieter Schwarz Foundation HAI Professor and Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence
BioYejin Choi is the Dieter Schwarz Foundation Professor and Senior Fellow at the Department of Computer Science at Stanford University and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) respectively. Choi is MacArthur Fellow (class of 2022), AI2050 Senior Fellow (class of 2024), and named among Time100 Most Influential People in AI in 2023. In addition, Choi is a co-recipient of 2 Test-of-Time awards and 8 Best and Outstanding Paper Awards at top AI conferences including ACL, ICML, NeurIPS, ICCV, CVPR, and AAAI, the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI’s 10 to Watch in 2016. Choi was a main stage speaker at TED 2023, and a keynote speaker for a dozen conferences across several AI disciplines including ACL, CVPR, ICLR, MLSys, VLDB, WebConf, and AAAI. Her current research interests include fundamental limits and capabilities of large language models, alternative training recipes for language models, symbolic methods for neural networks, reasoning and knowledge discovery, moral norms and values, pluralistic alignment, and AI safety.
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Angele Christin
Associate Professor of Communication, by courtesy, of Sociology and Senior Fellow at the Stanford Institute for Human-Centered AI
Current Research and Scholarly InterestsAngèle Christin studies the social and cultural impact of algorithms and artificial intelligence.
Her award-winning book, Metrics at Work: Journalism and the Contested Meaning of Algorithms (Princeton University Press, 2020) examined the dramatic transformations of journalism with the rise of social media platforms, metrics, and algorithms. Drawing on ethnographic methods, Angèle compared how American and French journalists made sense of traffic numbers, which in turn came with distinct effects on the production of news in the two countries.
Her most recent project examines the paradoxes of algorithmic labor through a study of influencers and influencer marketing on YouTube, Instagram, and TikTok.
