School of Medicine
Showing 701-750 of 950 Results
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Sheng Liu
Postdoctoral Scholar, Biomedical Data Sciences
BioSheng Liu is a postdoctoral fellow at Stanford University. In May 2023, He received a Ph.D. degree from New York University, majoring in Data Science and Machine Learning. His background is in the area of robust and trustworthy machine learning, machine learning for healthcare.
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Wanlu Liu
Affiliate, Institute for Immunity, Transplantation, and Infection Operations
Visiting Scholar, Institute for Immunity, Transplantation, and Infection OperationsBioAcademic Appointments
- Associate Professor, Zhejiang University
- Visiting Scholar, Stanford University School of Medicine (Mark M. Davis Lab)
Professional Education
- Ph. D., UCLA, Molecular Biology/Bioinformatics (2018)
- B.Med., Zhejiang University School of Medicine (2013)
Selected Publications:
- Ruonan Tian#, Zhejian Yu#, Ziwei Xue, Lize Wu, Jiaxin Wu, Shuo Cai, Bing Gao, Bing He, Yu Zhao, Jianhua Yao, Linrong Lu, Wanlu Liu*; Evaluation of T cell receptor construction methods from scRNA-seq data, Genomics Proteomics & Bioinformatics, 2025, 22(6), qzae086
- Ziwei Xue#, Lize Wu#, Ruonan Tian, Bing Gao, Yu Zhao, Bing He, Di Sun, Bingkang Zhao, Yicheng Li, Kaixiang Zhu, Lie Wang, Jianhua Yao*, Wanlu Liu*, Linrong Lu*; Integrative Mapping of Human CD8+ T Cell in Inflammation and Cancer, Nature Methods, 2025, 22(2), 435-445
- Jian Zhang#, Ruonan Tian#, Jie Yuan#, Zhang Siwen, Zhexu Chi, Weiwei Yu, Qianzhou Yu, Zhen Wang, Sheng Chen, Mobai Li, Dehang Yang, Tianyi Hu, Qiqi Deng, Xiaoyang Lu, Xue Zhang, Jia Liu, Wanlu Liu*, Di Wang*; A two-front nutrient supply environment fuels small intestinal physiology through differential regulation of nutrient absorption and host defense, Cell, 2024, 187(22), 6251-6271.
- Yaqi Su, Zhejian Yu, Siqian Jin, Zhipeng Ai, Ruihong Yuan, Xinyi Chen, Ziwei Xue, Yixin Guo, Di Chen, Hongqing Liang, Zuozhu Liu, Wanlu Liu*; Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data, Nature Communications, 2024, 15, 3972
- Yixin Guo, Ziwei Xue, Meiting Gong, Siqian Jin, Xindi Wu, Wanlu Liu*; CRISPR-TE: a web-based tool to generate single guide RNAs targeting transposable elements, Mobile DNA, 2024, 14(1), 3
- Xinyu Xiang#, Yu Tao#, Jonathan DiRusso, Fei-Man Hsu, Jinchun Zhang, Ziwei Xue, Julien Pontis, Didier Trono, Wanlu Liu*, Amander T. Clark*; Human reproduction is regulated by retrotransposons derived from ancient Hominidae-specific viral infections, Nature Communications, 2022
- Lize Wu#, Ziwei Xue#, Siqian Jin, Jinchun Zhang, Yixin Guo, Yadan Bai, Xuexiao Jin, Chaochen Wang, Lie Wang, Zuozhu Liu, James Q. Wang, Linrong Lu*, Wanlu Liu*; huARdb: human Antigen Receptor database for interactive clonotype-transcriptome analysis at the single-cell level, Nucleic Acids Research, 2021, gkab857
- Yixin Guo, Ziwei Xue, Ruihong Yuan, Jingyi Jessica Li, William A. Pastor, Wanlu Liu*; RAD: a web application to identify region associated differentially expressed genes, Bioinformatics, 2021, 37(17): 2741-2743. -
Wendy Liu, MD, PhD
Assistant Professor of Ophthalmology
Current Research and Scholarly InterestsDr. Liu's research interests include the role of mechanosensation in the eye as it relates to the pathophysiology of glaucoma, with the goal of finding new druggable targets in glaucoma treatment.
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Xin Liu
Basic Life Science Research Scientist, Genetics
BioXin Liu is a postdoctoral Research Scientist in the Department of Genetics at Stanford University. Xin holds a PhD in Chemistry from the University of Michigan, Ann Arbor. Her basic research interests include RNA and protein biochemistry, enzymology, cancer immunology, and autoimmune disease. She has published papers in several prestigious journals in the field of biochemistry, including Nature Communications, Journal of American Chemical Society, and Nucleic Acids Research. The highlight of her multidisciplinary research includes the development of high-throughput enzymatic methods to discover anti-microbial agents and to reveal mechanisms behind human mitochondrial diseases, as well as innovative applications of genome engineering and machine-learning to decode principles of RNA editing in human cells. Her current research focuses on the mechanistic study of innate immune pathways.
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Yang Merik Liu
Instructor, Psychiatry and Behavioral Sciences
Postdoctoral Scholar, PsychiatryBioDr. 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). -
Y. Lucy Liu, MD, PhD
Affiliate, Pediatrics - Hematology/Oncology
BioDr. Liu received her medical and clinical pharmacology training at two of the most prestigious medical institutions in China—West China School of Medicine (Sichuan University) and Peking Union Medical College (PUMC). She possesses extensive research experience in human molecular genetics, tumor biology, and experimental therapeutics.
Dr. Liu is internationally recognized for her expertise in two rare pediatric hematologic disorders: juvenile myelomonocytic leukemia (JMML) and Diamond-Blackfan anemia (DBA). She has authored more than 30 peer-reviewed publications in leading scientific journals, including Nature Genetics, Blood, Leukemia, and the Journal of Clinical Investigation (JCI). At her previous institutions, Dr. Liu served as a Principle Investigator (PI) and co-investigator on several NIH-funded research projects. In 2019, Dr. Liu joined the Department of Pediatrics at the Stanford University School of Medicine as a Senior Research Scientist. She recently developed a novel mouse model for DBA, which has proven to be a valuable tool for evaluating potential therapeutic strategies. Her current research focuses on elucidating the molecular pathogenesis of DBA and developing innovative treatment approaches for DBA. Since joining Stanford, Dr. Liu has published multiple manuscripts highlighting her ongoing research contributions. -
Amy Lo
Adjunct Clinical Associate Professor, Pathology
BioDr. Amy Lo is a pathologist with board certification in anatomic pathology, clinical pathology and molecular genetic pathology. She completed her MD and MS at the University of Illinois at Chicago and her residency in both anatomic and clinical pathology at Northwestern University. She then joined the faculty at Northwestern University as a Clinical Instructor and Advanced Gastrointestinal/Surgical Pathology Fellow. Amy then completed a molecular genetic pathology fellowship at Stanford University.
In 2016, Amy joined Genentech as research pathology scientist supporting drug research and development with a focus in oncology and individualized drug development.
Additionally, Amy continues clinical work as an Adjunct Clinical Associate Professor in pathology at Stanford University and Lucille Packard’s Children’s Hospital. -
Clara Lo
Clinical Professor, Pediatrics - Hematology & Oncology
Current Research and Scholarly InterestsResearch interests include:
Biomarkers and targeted therapy in pediatric immune thrombocytopenia
Transfusion-related iron overload
Hemophilia and other rare bleeding disorders
Thrombophilia -
Michelle Lo
Clinical Assistant Professor, Medicine
BioDr. Michelle Lo MD, FACP is a Clinical Assistant Professor in the Division of Hospital Medicine and Stanford School of Medicine. Growing up in Taiwan and in the Bay Area, she received her undergraduate degree in Molecular and Cellular Biology at University of California Berkeley, and her medical degree at David Geffen School of Medicine at UCLA. She then moved to NYU Grossman School of Medicine for her residency in Internal Medicine. She continued as Clinical Assistant Professor at NYU Grossman School of Medicine-Tisch Hospital from 2019-2020. After working in NYC during the COVID-19 pandemic, she returned to California to continue her career at Kaiser Permanente Santa Clara as a Hospitalist and affiliate Clinical Instructor at Stanford School of Medicine from 2020-2025, where she co-developed the Point of Care Ultrasound curriculum and was awarded the Hospitalist Teaching Award 3 years. She joined the Stanford School of Medicine Faculty in 2025. Her interests include medical education, curricular development, and use of Point-of-Care Ultrasound in the care of hospitalized patients.
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Nathan Lo
Assistant Professor of Medicine (Infectious Diseases) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsOur research group is interested in studying the transmission of infectious diseases and impact of public health interventions with an ultimate goal of informing public health policy. We study a diverse set of pathogens, both domestically and internationally, including vaccine-preventable infections (including COVID-19) and neglected parasitic diseases (such as schistosomiasis). Our group applies diverse computational methodologies, including tools from fields of epidemiology, mathematical and statistical modeling, simulation, and policy analysis.
A large emphasis of our work is translating scientific evidence into public health policy. Our track record includes multiple studies that have changed policy in the fields of neglected parasitic diseases and COVID-19. We work closely with policy organizations like the World Health Organization and the California Department of Public Health. Nathan was the lead writer of the World Health Organization guidelines on schistosomiasis (2022) and strongyloidiasis (2024).
Our current research focuses on the following areas:
(1) Vaccine-preventable infectious diseases (including measles and COVID-19) in the United States, with a focus on studying vaccines, transmission dynamics, and re-emergence of vaccine-eliminated diseases (emphasis on measles)
(2) Public health strategies for control and elimination of globally important neglected infectious diseases, such as helminths infections (schistosomiasis, strongyloidiasis) and typhoid fever
Our current NIH funded projects include:
(1) Real-time predictive modeling for public health departments to control infectious diseases (DP2 AI170485, PI: Lo)
(2) Precision mapping of Schistosoma mansoni risk for targeted public health control and elimination (R01 AI179771, PI: Lo)
Hiring
We are seeking to fill multiple research positions at all levels. Candidates interested in working on computational public health research related to infectious diseases with a strong quantitative background are highly encouraged to apply. If you an interested, please submit a cover letter, CV, and names of two references to Nathan.Lo@stanford.edu.
