School of Medicine
Showing 1-8 of 8 Results
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Irogue I Igbinosa
Instructor, Obstetrics & Gynecology - Maternal Fetal Medicine
Masters Student in Epidemiology and Clinical Research, admitted Autumn 2022BioIrogue Igbinosa, MD is a Maternal-Fetal Medicine fellow at Stanford University. She graduated from the University of Houston and earned her medical degree at Baylor College of Medicine. She subsequently completed her residency in Obstetrics and Gynecology Residency at Louisiana State University School of Medicine Baton Rouge. After residency, she was an AAMC-CDC Public Health Policy Fellow able to serve in the CDC Emergency Operations Center and contribute to research for health care providers regarding the management of the Zika virus in pregnant persons. Dr. Igbinosa's current research interests include severe maternal morbidity and mortality, health disparities and equity, anemia in pregnancy, infectious diseases, and clinical trials.
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Asef Islam
Masters Student in Biomedical Informatics, admitted Winter 2023
Masters Student in Computer Science, admitted Autumn 2022Current Research and Scholarly InterestsAI in medicine and other fields, particularly ML and CV techniques
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Haruka Itakura
Assistant Professor of Medicine (Oncology)
BioDr. Itakura is an Assistant Professor of Medicine (Oncology) in the Stanford University School of Medicine and practicing oncologist at the Stanford Cancer Center with background in biomedical informatics. She is a physician-scientist whose research mission is to drive medical advances at the intersection of cancer and data science research. Specifically, she aims to innovate state-of-the-art technologies to extract clinically useful knowledge from heterogeneous multi-scale biomedical data to improve diagnostics and therapeutics in cancer. She is a board-certified hematologist-oncologist and informaticist with specialized training in basic science, health services, and translational research. Her clinical background in oncology and PhD training in Biomedical Informatics position her to develop and apply data science methodologies on heterogeneous, multi-scale cancer data to extract actionable knowledge that can improve patient outcomes. Her ongoing research to develop and apply cutting-edge knowledge and skills to pioneer new robust methodologies for analyzing cancer big data is being supported by an NIH K01 Career Development Award in Biomedical Big Data Science. Her research focuses on developing and applying machine learning frameworks and radiogenomic approaches for the integrative analysis of heterogeneous, multi-scale data to accelerate discoveries in cancer diagnostics and therapeutics. Projects include prediction modeling of survival and treatment response, biomarker discovery, cancer subtype discovery, and identification of new therapeutic targets.
