Institute for Human-Centered Artificial Intelligence (HAI)
Showing 11-20 of 256 Results
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Heidi Baumgartner
ManyBabies Executive Director
Current Research and Scholarly InterestsAs the executive director of the ManyBabies global consortium (manybabies.org), I am interested in facilitating Big Team Science practices to address difficult outstanding theoretical and methodological questions about the nature of early development and how it is studied.
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Mohsen Bayati
Carl and Marilynn Thoma Professor in the Graduate School of Business and Professor, by courtesy, of Electrical Engineering
Current Research and Scholarly Interests1) Healthcare management: I am interested in improving healthcare delivery using data-driven modeling and decision-making.
2) Network models and message-passing algorithms: I work on graphical modeling ideas motivated from statistical physics and their applications in statistical inference.
3) Personalized decision-making: I work on machine learning and statistical challenges of personalized decision-making. The problems that I have worked on are primarily motivated by healthcare applications. -
Gill Bejerano
Professor of Developmental Biology, of Computer Science, of Pediatrics (Genetics) and of Biomedical Data Science
Current Research and Scholarly Interests1. Automating monogenic patient diagnosis.
2. The genomic signatures of independent divergent and convergent trait evolution in mammals.
3. The logic of human gene regulation.
4. The reasons for sequence ultraconservation.
5. Cryptogenomics to bridge medical silos.
6. Cryptogenetics to debate social injustice.
7. Managing patient risk using machine learning.
8. Understanding the flow of money in the US healthcare system. -
Eran Bendavid
Professor of Medicine (Primary Care and Population Health), of Health Policy, Senior Fellow at the Woods Institute for the Environment and, by courtesy, at the Freeman Spogli Institute for International Studies
Current Research and Scholarly InterestsEffect of global health policies on health of individuals in developing countries, global health, HIV and TB.
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Jon Bernstein
Professor of Pediatrics (Genetics) and, by courtesy, of Genetics
Current Research and Scholarly InterestsMy research is focused on the diagnosis, discovery and delineation of rare genetic conditions with a focus on neurodevelopmental disorders. This work includes the application of novel computational methods and multi-omics profiling (whole genome sequencing, long-read DNA sequencing, RNA sequencing, methylomics, metabolomics). I additionally participate in an interdisciplinary project to develop induced pluripotent stem cell and assembloid models of genetic neurodevelopmental disorders.
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Michael Bernstein
Associate Professor of Computer Science and Senior Fellow at the Stanford Institute for HAI
BioMichael Bernstein is an Associate Professor of Computer Science at Stanford University, where he is a Bass University Fellow and Interim Director of the Symbolic Systems program. His research focuses on designing social, societal, and interactive technologies. This research has been reported in venues such as The New York Times, Wired, Science, and Nature. Michael has been recognized with an Alfred P. Sloan Fellowship, the UIST Lasting Impact Award, and the Computer History Museum's Patrick J. McGovern Tech for Humanity Prize. He holds a bachelor's degree in Symbolic Systems from Stanford University, as well as a master's degree and a Ph.D. in Computer Science from MIT.
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Vasiliki (Vicky) Bikia
Postdoctoral Scholar, Biomedical Informatics
BioDr. Vasiliki Bikia is a Fellow at the Institute for Human-Centered Artificial Intelligence and Postdoctoral Scholar at Stanford University, working with Prof. Roxana Daneshjou. She received her Advanced Diploma degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTH), Greece, in 2017, and her Ph.D. degree in Biomedical Engineering from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland, in 2021. Her Ph.D. research addressed the clinical need for providing non-invasive tools for cardiovascular monitoring leveraging machine learning and physics-based numerical modeling.
Her current work focuses on developing large multimodal models to enhance biomarker identification and patient outcome prediction. At Stanford, she has also contributed to the Stanford Spezi framework, designing and prototyping the Spezi Data Pipeline tool for enhanced digital health data accessibility and analysis workflows. Her research interests include health algorithms, clinical and digital biomarkers, machine learning, non-invasive monitoring, and the application of large language models for personalized healthcare, predictive analytics, and enhancing patient-clinician interactions.
