Stanford University
Showing 18,081-18,100 of 35,010 Results
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Yang Merik Liu
Instructor, Psychiatry and Behavioral Sciences
BioDr. Yang Merik Liu is currently an Instructor with the Department of Psychiatry and Behavioral Sciences, Stanford University, and is affiliated with the Center for Machine Vision and Signal Analysis, University of Oulu, Finland. He is a Co-I of the NIH/NIA R33 Grant, and was a PI of the North Ostrobothnia Regional Fund of the Finnish Cultural Foundation and the Instrumentarium Science Foundation, carrying out research on digital measures with affective intelligence. Dr. Liu coordinated and managed "AI Forum" and "ICT 2023 TrustFace" projects during his postdoctoral research in University of Oulu since Jan. 2022, led by Academy Professor Guoying Zhao, member of Academia Europaea, member of the Finnish Academy of Sciences and Letters, IEEE/IAPR/ELLIS Fellow. He was also a former researcher with the Haaga-Helia University of Applied Sciences, in 2023, and was a visiting scholar with Hong Kong Baptist University (Prof. Pong Chi Yuen) and University of Cambridge (Prof. Hatice Gunes), in 2023 and 2024, respectively. Dr. Liu has published more than 40 papers in reputable journals and proceedings. He served as the Session Chair of IEEE FG 2025, the Track Chair of IEEE COINS 2026, the Guest Associate Editor of Frontiers in Psychology and Frontiers in Human Neurosciences, and organized tutorials and workshops in international conferences, i.e., HHAI 2024 and IEEE FG 2025. Dr. Liu was an Assistant Lecturer of the "Affective Computing" course in University of Oulu, in 2023. He mentored junior doctoral researchers and co-supervised post-/undergraduate students. His research interests include affective computing, cognitive computation for cross-species behavioral, and AI for aging medicine.
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Yongkai Liu
Instructor, Radiology
BioDr. Yongkai Liu is an instructor in the Department of Radiology, Division of Neuroimaging and Neurointervention at Stanford University. His research focuses on developing and evaluating advanced techniques to improve treatment decision-making and prognostication in brain diseases—particularly stroke—using imaging and deep learning. Dr. Liu is a recipient of the prestigious K99/R00 award for his work on integrating large language models with imaging-based deep learning for stroke outcome prediction.
Prior to joining Stanford, Dr. Liu earned his Ph.D. in Physics and Biology in Medicine from UCLA under the mentorship of Prof. Kyung Sung. This rigorous training equipped him with a strong foundation in medicine, deep learning, and physics. His Ph.D. thesis, titled “Advancing Segmentation and Classification Methods in Magnetic Resonance Imaging via Artificial Intelligence,” focused on developing cutting-edge deep learning and machine learning techniques for MRI-based clinical applications. During his master’s studies, he conducted research on CT Virtual Colonoscopy under the guidance of Prof. Jerome Liang, an IEEE Fellow.
Dr. Liu has also made significant contributions to the academic community as a peer reviewer for leading journals, including The Lancet Digital Health, NPJ Digital Medicine, Medical Image Analysis, Medical Physics, Scientific Reports, British Journal of Radiology, BJR|Artificial Intelligence, Annals of Clinical and Translational Neurology, IEEE Transactions on Medical Imaging, IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Radiation and Plasma Medical Sciences, IEEE Transactions on Biomedical Engineering, and IEEE Transactions on Neural Networks and Learning Systems.
Dr. Liu is an emerging leader in neuroimaging, stroke research, and artificial intelligence. His work has earned wide recognition, including the K99/R00 Award, the 2024 AJNR Lucien Levy Award, the David M. Yousem Research Fellow Award in both 2024 and 2025, and selection as a semi-finalist for the 2024 Cornelius G. Dyke Award. These honors underscore his promise as an investigator and his potential to make significant contributions to the field (https://med.stanford.edu/rsl/news/yongkai-liu-receives-research-fellow-award.html). His research has also been featured in Neurology Today, including his early work linking chain-of-thought methods with clinical reasoning to help advance the use of large language models in real-world clinical care (https://neurologytoday.aan.com/doi/10.1212/netod-blogs.10000031).
