Stanford University
Showing 301-344 of 344 Results
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Wan-Jin Lu
Basic Life Research Scientist, Stem Cell Bio Regenerative Med Institute
BioDr. Wan-Jin Lu is a Research Scientist in Dr. Phil Beachy's lab. Wan-Jin grew up in Taiwan, obtained her B.S. in Zoology at National Taiwan University and completed her PhD in Genetics and Development at UT Southwestern in the lab of Dr. John Abrams. Her Ph.D. research involved the identification of the evolutionary conserved function of the tumor suppressor gene p53 that ensures the quality control of germ cells. She then moved to the Bay Area, where she was a Damon Runyon Postdoctoral Fellow in the Institute of Stem Cell Biology and Regenerative Medicine in the Beachy lab. Her work currently focuses on understanding the function of Hedgehog signaling in taste receptor cell homeostasis and delineating the mechanisms of taste receptor regeneration after chemotherapy-induced loss.
Since 2017, she has been collaborating with Tabula Muris And Tabula Sapiens Consortium to investigate taste receptor stem cell renewal and regeneration in the Beachy lab. Her work has received funding support from California Institute of Regenerative Medicine (CIRM), Thomas and Stacey Siebel Foundation, and NIH (R21 and R01). -
Linda Lucian
Translational Program Manager, School of Medicine - MDRP'S - Biodesign Program
Current Role at StanfordPrimary Biodesign role of Translation Project Manager for the eight internal funding programs administered through Biodesign. Stanford- Coulter TRPP Award, NIH funded Spectrum-Medtech Award, Wu Tsai Neuroscience:Translate Award, Innovation Fellowship Extension Award, Innovation Course Extension Award, Faculty Fellowship Award, NEXT Award, and Robert Howard Next Step Award.
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Gabriela Luna-Victoria
Adm Assoc 3, Pediatrics - Neonatology
Current Role at StanfordAdministrative Associate in the Department of Pediatrics, Division of Neonatal and Developmental Medicine at Stanford School of Medicine
Administrative support to: Jochen Profit, Gary Darmstadt, Suzan Carmichael, Anca Pasca -
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
