School of Medicine
Showing 81-100 of 164 Results
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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.
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Liam Edward Mulhall
Software Developer, Biomedical Data Science
Current Role at StanfordLiam develops and maintains the HLA Curation Interface, a tool that supports the assessment of HLA alleles and haplotypes for use in precision medicine and research. He also works on internal tools used by the Stanford ClinGen team.
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Fateme (Fatima) Nateghi
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoc researcher at the Division of Computational Medicine, I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I explored the complexities of machine learning algorithms and their transformative potential in clinical settings. My research focused on adapting these algorithms for time-to-event data, a method used to predict when specific events may occur in a patient’s future.
At Stanford, my work centers on building trustworthy AI systems to enhance healthcare delivery. I develop and evaluate machine learning models that integrate structured electronic health records (EHRs) and unstructured clinical notes to support real-world clinical decision-making. My recent projects include predicting treatment retention in opioid use disorder, improving antibiotic stewardship for urinary tract infections, and enabling digital consultations through large language models (LLMs). I'm particularly interested in embedding-based retrieval and retrieval-augmented generation (RAG) methods that help bridge cutting-edge AI research with clinical practice.
My role involves not just advancing the integration of machine learning in healthcare but also collaborating with a diverse team of clinicians, data scientists, and engineers. Together, we're striving to unravel complex healthcare challenges and ultimately improve patient outcomes. -
Jeff Nirschl
Affiliate, Biomedical Data Science
BioJeff Nirschl, M.D., Ph.D. is an Instructor in Pathology at Stanford University, Stanford, CA with clinical expertise in Neuropathology. He completed his Ph.D. in Neuroscience at the University of Pennsylvania under the supervision of Dr. Erika Holzbaur. During his thesis research, he investigated axonal transport and genetic forms of parkinsonism. He also developed computational image analysis workflows for fluorescence microscopy and digital pathology. His research interests include molecular motors and the neuronal cytoskeleton, the regulation of axonal transport in neurodegeneration, digital pathology, and quantitative image analysis using machine learning.
https://orcid.org/0000-0001-6857-341X -
Humaira Noor
Postdoctoral Scholar, Biomedical Informatics
BioDr. Humaira Noor is a postdoctoral researcher in the Gevaert Lab with a PhD in glioma genomics from University of New South Wales, Australia. Her expertise spans biomarker discovery, with particular emphasis on prognostic and molecular determinants of glioma treatment-response, radiogenomic model development for early high-risk patient stratification, and the integration of multi-omics and biomedical imaging to advance precision oncology
