Biomedical Data Science
Showing 81-100 of 179 Results
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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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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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Pan Lu
Postdoctoral Scholar, Biomedical Data Sciences
Current Research and Scholarly InterestsMy research goal is to build machines that can reason and collaborate with humans for the common good. My primary research focuses on machine learning and NLP, particularly machine reasoning, mathematical reasoning, and scientific discovery:
1. Mathematical reasoning in multimodal and knowledge-intensive contexts
2. Tool-augmented large language models for planning, reasoning, and generation
3. Parameter-efficient fine-tuning for fondation models
4. AI for scientific reasoning and discovery -
Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Matthew Lungren is a physician-scientist and AI leader whose work has helped shape modern multimodal healthcare AI from early research through large-scale deployment. He joined Stanford University in 2014 as clinical research faculty, where he led a fully dedicated pediatric interventional radiology clinical service and established an NIH- and industry-supported clinical AI research program that helped catalyze what became the Stanford Center for AI in Medicine & Imaging. He remains an Adjunct Professor of Biomedical Data Science at Stanford and also holds a part-time clinical appointment at UCSF.
Dr. Lungren has authored more than 200 peer-reviewed publications with more than 35,000 citations, and he has taught more than 100,000 learners through AI-in-healthcare courses across platforms including Coursera and LinkedIn Learning. His broader contributions include advancing multimodal imaging-plus-EHR approaches, open-sourcing AI-ready medical imaging datasets and models, and serving in national leadership roles across the radiology AI community. After a sabbatical in 2021, he transitioned from academia to industry and joined Microsoft, where he served in senior leadership roles including Chief Scientific Officer for Microsoft Health & Life Sciences. At Microsoft, he founded and led cross-company teams that shipped multimodal healthcare foundation models and agentic, auditable generative AI workflows into production, including healthcare agent orchestration capabilities and major EHR partnerships, and led the health and life sciences partnerships with OpenAI.
Dr. Lungren is also a top rated instructor leading AI in Healthcare courses designed especially for learners with non-technical backgrounds:
Stanford/Coursera: https://www.coursera.org/learn/fundamental-machine-learning-healthcare
LinkedIn Learning: https://www.linkedin.com/learning/an-introduction-to-how-generative-ai-will-transform-healthcare -
Daniel Mas Montserrat
Affiliate, Biomedical Data Science
BioDaniel Mas Montserrat holds a PhD in Electrical and Computer Engineering from Purdue University. Previously he graduated summa cum laude from the Polytechnic University of Catalonia in Audiovisual Systems in Telecommunications Engineering. Currently, he is a research fellow at the Stanford School of Medicine (Department of Biomedical Data Science). His research focuses on applying computational methods to problems in population genetics and biomedicine.
