School of Medicine
Showing 1-6 of 6 Results
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David Iberri
Clinical Assistant Professor, Medicine - Hematology
BioDr. Iberri is a hematologist who specializes in the treatment of multiple myeloma, Waldenström macroglobulinemia, and other blood and bone marrow disorders. He is actively involved in clinical research evaluating novel agents in hematologic malignancies. His research interests include the development and application of biomarkers to select patients most likely to benefit from therapy, and in the development of blood tests to reduce the need for bone marrow biopsies in myeloma disease monitoring.
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Gentaro Ikeda
Instructor, Medicine - Cardiovascular Medicine
BioDr. Ikeda is a physician-scientist who develops innovative diagnostic and therapeutic modalities for patients with cardiovascular disease. Based on his clinical experience as a cardiologist, he has become aware of major clinical shortcomings, specifically in the current pharmaceutical therapies for myocardial infarction (MI) and chronic heart failure (HF). Some evidence-based drug therapies, including β-blockers, ivabradine, and renin-angiotensin-aldosterone antagonists are difficult to apply to critical patients due to adverse side effects. Drugs that have shown efficacy in basic animal experiments have failed to show significant benefits in clinical trials. To address these problems, he moved to academia to conduct translational research. During his graduate training in the Egashira Lab, he focused on drug delivery systems (DDS) that target mitochondria in animal models of MI. He obtained advanced skills in molecular biology, mitochondrial bioenergetics, and animal surgery. He realized the importance of translational research and the great potential of DDS to overcome many clinical problems. He developed nanoparticle-mediated DDS containing cyclosporine for the treatment of patients with MI. He published a first-author paper and received academic awards for his novel science. Since becoming a postdoctoral fellow in the Yang Lab, he has continued to build upon his previous training in translational research. He is currently developing an innovative therapy, namely, extracellular vesicles-mediated mitochondrial transfer for mitochondria-related diseases such as heart failure and mitochondrial disease.
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John P.A. Ioannidis
Professor of Medicine (Stanford Prevention Research), 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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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 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, multi-omic (e.g., radiomic, genomic, transcriptomic) data to accelerate discoveries in cancer diagnostics and therapeutics. Her current projects include prediction modeling of survival, treatment response, recurrence, and CNS metastasis in different cancer subtypes; detection of occult invasive breast cancer; and identification of novel therapeutic targets. Her ultimate goal is to be able to translate her research findings back to the clinical setting for the benefit of patients with difficult-to-treat cancers.