Stanford University
Showing 14,161-14,170 of 37,043 Results
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Henry Francis Isselbacher
Research Administrator, School of Medicine - Biomedical Ethics
BioHenry Isselbacher is a Research Administrator at the Laurie J. Girand Center for Biomedical Ethics at Stanford. He graduated from the University of California, Berkeley, in May 2024 with a degree in Economics and Public Health, earning honors in Public Health for his senior thesis titled "Vacancy Rates in US Hospitals with Workplace Violence Prevention Programs." As an undergraduate, Henry developed an extensive background in financial reporting and budgeting as CFO for UC Berkeley's student association and co-chair of several student fee committees. After graduating, Henry worked in the Division of Student Affairs at Cal as a Special Projects Coordinator, where he focused on efforts to streamline the accessibility and awareness of funding and other resources for students and student organizations.
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Haruka Itakura, MD, PhD
Assistant Professor of Medicine (Oncology)
BioDr. Haruka Itakura is an Assistant Professor of Medicine (Oncology) in the Stanford University School of Medicine, a data scientist, and a practicing breast medical oncologist at the Stanford Women’s Cancer Center. She is board-certified in Oncology, Clinical Informatics, Hematology, and Internal Medicine. Her research mission is to drive medical advances at the intersection of cancer and data science, applying state-of-the-art machine learning/artificial intelligence (AI) techniques to extract clinically actionable knowledge from heterogeneous multi-scale cancer data to improve patient outcomes. Her ongoing research to develop robust methodologies and apply cutting-edge techniques to analyze complex cancer big data was catapulted by an NIH K01 Career Development Award in Biomedical Big Data Science after obtaining a PhD in Biomedical Informatics at Stanford University. Her cancer research focuses on extracting radiomic (pixel-level quantitative imaging) features of tumors from medical imaging studies and applying machine learning frameworks, including radiogenomic approaches, for the integrative analysis of heterogeneous, multiomic (e.g., radiomic, genomic, transcriptomic) data to accelerate discoveries in cancer diagnostics and therapeutics. Her current projects include data science/AI-powered prediction modeling of survival, treatment response, cancer recurrence, and metastasis in different cancer subtypes; detection of occult invasive breast cancer; and identification of novel therapeutic targets. Her goal is to translate her research findings back to the clinical setting for the benefit of patients with difficult-to-treat cancers.
