School of Medicine
Showing 101-110 of 195 Results
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Samuel Montalvo Hernandez
Postdoctoral Scholar, Cardiovascular Medicine
BioI am a clinical exercise physiologist and sport biomechanist interested in human exercise and sports performance. I am a certified performance and sport scientist (CPSS) and a certified strength and conditioning specialist with distinction (CSCS, *D) by the National Strength and Conditioning Association (NSCA). In 2022, I was honored with the 2022 Wu Tsai Human Performance Alliance Post-Doctoral Research Fellowship. As a research exercise and sport scientist, I am interested in understanding the mechanical, molecular, and physiological mechanisms of human performance. Additionally, I am interested in creating new and practical training methods to improve human exercise and sports performance.
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Eric Mou, MD
Postdoctoral Medical Fellow, Oncology
BioI was born in Oregon and raised in Iowa, where I cultivated my initial interest in science and medicine. I completed my undergraduate degree and medical school at the University of Iowa before heading to Stanford University for my internal medicine residency and oncology fellowship training. I chose this field to try my best in assisting patients during times of great need, and working to understand what is of greatest importance to them as they navigate their unique journey of cancer care. My clinical focus is in the care of patients with lymphoma and other hematologic cancers. My scholarly interests include better understanding the efficacy cancer therapeutics, improving patients' experience as the proceed through treatment, and promoting strength in medical education.
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Tushar Mungle
Postdoctoral Scholar, Biomedical Informatics
Current Research and Scholarly InterestsUse electronic health records (EHRs) to identify and classify common ocular diseases such as glaucoma, diabetic retinopathy, and macular degeneration. We aim to develop an approach to accurately identify these conditions using EHRs. This will be followed by cluster analysis to identify novel subtypes of these conditions that have not been recognized before. Finally, we will develop an approach to extract outcome data from EHRs for patients with these conditions in the primary care setting.
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Michitaka Nakano
Postdoctoral Scholar, Hematology
BioI am a MD/PhD postdoctoral fellow and medical oncologist with a long-standing interest in translational cancer research. My long-term goal is to be a lab-based physician-scientist and independent academic researcher, translating basic cancer research, and mentoring next-generation scientists. My thesis work in Japan focused on cancer stem cell equilibrium by uniquely applying organoid culture as a method to elucidate cancer stem cell dynamics, which was awarded in Japanese Cancer Association. Along with the development of the field represented by success in T cell checkpoint, my interest gradually shifted to immune oncology while I examined numerous numbers of cancer patients as a medical oncology fellow. My postdoctoral fellowship at Calvin Kuo Lab in Stanford (2019-present) focuses on tumor immune microenvironment. Kuo lab developed a unique 3D air-liquid interface (ALI) organoid system that cultures tumors while preserving their endogenous infiltrating immune cells (T,B ,NK, Myeloid cells). My postdoctoral work will prove the significance of organoids as a translational tool to discover tumor-immune interaction by novel checkpoint inhibitors for immune cells, which can be broadly applicable to basic cancer biology, precision medicine, therapeutics validation and biomarker discovery.
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Fateme Nateghi Haredasht
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research, I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I delved into the complexities of machine learning algorithms and their transformative potential in healthcare settings. My research, particularly focused on adapting these algorithms for time-to-event data (a method used for predicting specific events in a patient’s future), has not only been a challenging endeavor but also a deeply fulfilling one.
Now at Stanford, my role involves not just advancing machine learning integration in healthcare, but also collaborating with a diverse team of experts. Together, we're striving to unravel complex healthcare challenges and improve patient outcomes.
