Institute for Human-Centered Artificial Intelligence (HAI)
Showing 1-10 of 13 Results
Professor of Earth and Planetary Sciences and, by courtesy, of Geophysics
Current 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.
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.
Assistant Professor (Research) of Radiology (Integrative Biomedical Imaging Informatics at Stanford) and, by courtesy, 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 range from developing novel data-efficient machine learning algorithms to clinical deployment and validation of patient outcomes. He is also exploring combining imaging with clinical, natural language, and time series data.
Jonathan H. Chen, MD, PhD
Assistant Professor of Medicine (Biomedical Informatics)
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.
Associate Professor of Communication and, by courtesy, of Sociology
Current Research and Scholarly InterestsAngèle Christin studies how algorithms and analytics transform professional values, expertise, and work practices.
Her book, Metrics at Work: Journalism and the Contested Meaning of Algorithms (Princeton University Press, 2020) focuses on the case of web journalism, analyzing the growing importance of audience data in web newsrooms in the U.S. and France. Drawing on ethnographic methods, Angèle shows how American and French journalists make sense of traffic numbers in different ways, which in turn has distinct effects on the production of news in the two countries. She discussed it on the New Books Network podcast.
In a related study, she analyzed the construction, institutionalization, and reception of predictive algorithms in the U.S. criminal justice system, building on her previous work on the determinants of criminal sentencing in French courts.
Her new project examines the paradoxes of algorithmic labor through a study of influencers and influencer marketing on YouTube, Instagram, and TikTok.
James G. March Professor of Organizational Studies in Education and Business, Professor of Psychology and, by courtesy, of Organizational Behavior at the Graduate School of Business
Current Research and Scholarly InterestsMuch of my research examines processes related to identity maintenance and their implications for social problems. One primary aim of my research is the development of theory-driven, rigorously tested intervention strategies that further our understanding of the processes underpinning social problems and that offer solutions to alleviate them. Two key questions lie at the core of my research: “Given that a problem exists, what are its underlying processes?” And, “Once identified, how can these processes be overcome?” One reason for this interest in intervention is my belief that a useful way to understand psychological processes and social systems is to try to change them. We also are interested in how and when seemingly brief interventions, attuned to underlying psychological processes, produce large and long-lasting psychological and behavioral change.
The methods that my lab uses include laboratory experiments, longitudinal studies, content analyses, and randomized field experiments. One specific area of research addresses the effects of group identity on achievement, with a focus on under-performance and racial and gender achievement gaps. Additional research programs address hiring discrimination, the psychology of closed-mindedness and inter-group conflict, and psychological processes underlying anti-social and health-risk behavior.
Nicholas Alvaro Coles
BioI am a Research Scientist at Stanford University and the Director of the Psychological Science Accelerator. I conduct research on emotions, big team science, and [more recently] AI.
In affective science, I seek to understand the social, cognitive, and physiological processes that underlie emotion. Much of my research here has focused on examining the extent to which sensorimotor feedback from the peripheral nervous system (e.g., changes in heart rate and muscle tension) impact the conscious experience of emotion.
In big team science, I seek to build infrastructure that allows researchers to collaboratively tackle ultra-complex questions in science. In this domain, I (a) direct the Psychological Science Accelerator: a consortium of researchers (2500+ from 70+ countries) who pool resources to accelerate the accumulation of generalizable knowledge in psychology, (b) co-direct the Stanford Big Team Science Lab, and (c) support various big team science initiatives (e.g., the Virtual Experience Research Accelerator and Next Generation Event Horizon Telescope).
In artificial intelligence, I am interested in ways that these new technologies can be used to monitor, predict, and change humans' emotions. For example, I recently founded the Emotion Physiology and Experience Collaboration, which seeks to improve the algorithmic recognition of emotion by (a) documenting cultural and contextual sources of model bias, and (b) building foundational datasets that can improve these models.