School of Medicine
Showing 61-80 of 148 Results
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Teri Klein
Professor (Research) of Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics
Current Research and Scholarly InterestsCo-founder, Pacific Symposium on Biocomputing
NIEHS, Site Visit Reviewer
NIH, Study Section Reviewer -
Curtis Langlotz
Professor of Radiology (Thoracic Imaging), of Biomedical Informatics Research, of Biomedical Data Science and Senior Fellow at the Stanford Institute for HAI
Current Research and Scholarly InterestsI am interested in the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. My laboratory is developing deep neural networks that detect and classify disease on medical images. We also develop natural language processing methods that use the narrative radiology report to create large annotated image training sets for supervised machine learning experiments.
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Philip W. Lavori
Professor of Biomedical Data Science, Emeritus
Current Research and Scholarly InterestsBiostatistics, clinical trials, longitudinal studies, casual inference from observational studies, genetic tissue banking, informed consent. Trial designs for dynamic (adaptive) treatment regimes, psychiatric research, cancer.
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Laura C. Lazzeroni, Ph.D.
Professor (Research) of Psychiatry and Behavioral Sciences and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsStatistics/Data Science. I develop & apply models, methods & algorithms for complex data in medical science & biology. I am also interested in the interplay between fundamental statistical properties (e.g. variability, bias, p-values) & how scientists actually use & interpret data. My work in statistical genetics includes: the invention of Plaid bi-clustering for gene expression data; methods for twin, association, & family studies; multiple testing & estimation for high dimensional arrays.
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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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Ying Lu
Professor of Biomedical Data Science and, by courtesy, of Epidemiology
Current Research and Scholarly InterestsBiostatistics, clinical trials, statistical evaluation of medical diagnostic tests, radiology, osteoporosis, meta-analysis, medical decision making
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Cassie Ann Ludwig, MD, MS (she/her/hers)
Assistant Professor of Ophthalmology (Research/Clinical Trials)
Masters Student in Biomedical Data Science, admitted Autumn 2023Current Research and Scholarly InterestsMy research at present focuses on better understanding high and pathologic myopia and their retina sequelae (retinal detachments, myopic traction maculopathy, myopic macular degeneration) through informatics and data-driven research. My goal is to make discoveries within the field of Ophthalmology that will impact the rest of medicine, taking advantage of our ready access to the only visible portion of the central nervous system.
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Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Lungren is Chief Data Science Officer for Microsoft Health & Life Sciences where he focuses on translating cutting edge technology, including generative AI and cloud services, into innovative healthcare applications. As a physician and clinical machine learning researcher, he maintains a part-time clinical practice at UCSF while also continuing his research and teaching roles as adjunct professor at Stanford University.
Prior to joining Microsoft, Dr Lungren was a clinical interventional radiologist and research faculty at Stanford University Medical School where he led the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). He later served as Principal for Clinical AI/ML at Amazon Web Services in World Wide Public Sector Healthcare, focusing on business development for clinical machine learning technologies in the public cloud.
His scientific work has led to more than 150 publications, including work on multi-modal data fusion models for healthcare applications, new computer vision and natural language processing approaches for healthcare specific domains, opportunistic screening with machine learning for public health applications, open medical data as public good, prospective clinical trials for clinical AI translation, and application of generative AI in healthcare. He has served as advisor for early stage startups and large fortune-500 companies on healthcare AI technology development and go-to-market strategy. Dr. Lungren's work has been featured in national news outlets such as NPR, Vice News, Scientific American, and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare.
Dr. Lungren is also a top rated instructor on Coursera where his AI in Healthcare course designed especially for learners with non-technical backgrounds has been completed by more than 20k students around the world - enrollment is open now: https://www.coursera.org/learn/fundamental-machine-learning-healthcare
