School of Medicine
Showing 1,981-1,990 of 5,031 Results
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Alexander Ioannidis
Assistant Professor (Research) of Genetics and of Biomedical Data Science
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)BioDr. Ioannidis earned his Ph.D. from Stanford University in Computational and Mathematical Engineering together with an M.S. in Management Science and Engineering (Optimization). He graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil at the University of Cambridge from the Department of Applied Math and Theoretical Physics in Computational Biology. His research focuses on the design of algorithms and application of computational methods for problems in precision health, genomics, clinical data science, and AI in healthcare.
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John P.A. Ioannidis
Professor of Medicine (Stanford Prevention Research Center), of Epidemiology and Population Health and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsMeta-research
Evidence-based medicine
Clinical and molecular epidemiology
Human genome epidemiology
Research design
Reporting of research
Empirical evaluation of bias in research
Randomized trials
Statistical methods and modeling
Meta-analysis and large-scale evidence
Prognosis, predictive, personalized, precision medicine and health
Sociology of science -
Eric Ip
Clinical Professor, Medicine - Primary Care and Population Health
Current Research and Scholarly InterestsMy research interests include the use and abuse of anabolic steroids and other performance enhancing/cognitive enhancing drugs.
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Wui Ip, MD
Clinical Assistant Professor, Pediatrics
BioWui Ip, MD is a pediatrician and physician informaticist. He is interested in applying machine learning to support clinical decision making and improve patient care.
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Negaur Iranpour, MD
Clinical Assistant Professor, Radiology
BioDr. Iranpour is a board-certified, fellowship-trained radiologist with Stanford Health Care Radiology. She is also a clinical instructor in the Department of Radiology at Stanford University School of Medicine.
Dr. Iranpour’s practice focuses on image-guided interventions for a variety of conditions including cancers, tumors, and lesions. She treats a wide range of health concerns, including cancer pain, endometriosis, prostate cancer, soft tissue tumors, uterine fibroids, and Parkinson’s disease.
Dr. Iranpour’s research interests include using abdominal MRIs to evaluate liver lesions and studying the features of pancreatic cysts. She also studies the use of MRI-guided prostate ablation and cryoablation for desmoid tumors (noncancerous growths in connective tissues).
Dr. Iranpour has been published in many peer-reviewed journals, including Abdominal Radiology, Human Psychopharmacology: Clinical and Experimental, and the Asian Pacific Journal of Cancer Prevention. She has also presented her work at national and international meetings, including at the International Society for Magnetic Resonance in Medicine, the Society of Abdominal Radiology, and the American Public Health Association. Her papers and presentations have won awards, including at the 1st Basic and Clinical Neuroscience Congress in Iran.
Dr. Iranpour is a member of the American Board of Radiology, the American College of Radiology, and the Society of Abdominal Radiology. She is also a member of the Radiological Society of North America and the International Society for Magnetic Resonance in Medicine. -
Nicole Irgens-Moller
Clinical Assistant Professor, Emergency Medicine
Clinical Assistant Professor (By courtesy), PediatricsCurrent Research and Scholarly InterestsAssociation of Race and Insurance on Social Work Consults and Child Protective Services Reports following Ingestions in Young Children. [Platform Presentation]. Ray E. Helfer Society Conference, 2024, Savannah, GA, United States
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Md Tauhidul Islam
Assistant Professor of Radiation Oncology (Radiation Physics)
Current Research and Scholarly InterestsMy research focuses on developing computationally efficient and clinically reliable AI methods for biomedical imaging and high-dimensional molecular data, with an emphasis on cancer and neurological disease. The Islam Lab designs novel representations and learning frameworks that improve deep learning performance in data-constrained biomedical settings, including methods that transform tabular omics data into spatially meaningful representations.
