Stanford University
Showing 1,051-1,100 of 2,733 Results
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Bayan Kharrat
Postdoctoral Scholar, Developmental Biology
BioDr. Bayan Kharrat is a postdoctoral researcher in the Goins Lab at Stanford University School of Medicine, where she studies the mechanisms governing fate commitment in hematopoietic stem and progenitor cells in Drosophila, with a focus on identifying key regulatory factors involved in this process.
Dr. Kharrat earned her Ph.D. in Biology from the University of Szeged and conducted her graduate research at the HUN-REN Biological Research Centre in Szeged, where she investigated the dual role of Headcase, an imaginal cell factor, in maintaining progenitor cells in the larval lymph gland. Her expertise spans Drosophila genetics, developmental biology, molecular biology, and confocal microscopy. -
Aditi Khatpe
Postdoctoral Scholar, Pathology
BioAs a Postdoctoral Fellow, I study breast cancer progression and invasion. My research leverages high-dimensional spatial technologies to map cellular architecture and uncover how tumor–stroma interactions influence disease progression. Ultimately, my goal is to translate these insights into strategies that improve diagnosis and treatment.
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Alexey Khudorozhkov
Postdoctoral Scholar, Physics
Current Research and Scholarly InterestsAlexey Khudorozhkov works at the intersection of quantum many-body physics, quantum information, mathematics, and theoretical computer science. He is particularly interested in using mathematical tools, such as geometric group theory and graph theory, to answer questions about quantum dynamics, information, and computation. His recent work has focused on nonequilibrium quantum dynamics, nonergodic many-body systems, and reversible computation. His advisor is Vedika Khemani.
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Hyunkyung Claire Kim
Postdoctoral Scholar, Endocrinology and Metabolism
BioHyunkyung Claire Kim is a Postdoctoral Research Scholar in the Translational Genomics of Diabetes Lab led by Dr. Anna Gloyn. She received her PhD in Genetics from the University of Chicago, where she developed a statistical method to disentangle shared and trait-specific genetic architecture across complex diseases using large-scale biobank data. Prior to her doctoral training, she worked at Massachusetts General Hospital, studying the genetic subtypes and heterogeneity of type 2 diabetes through data-driven clustering approaches.
She is interested in the translational genomics of diabetes, including integrating human genetics with clinical data to uncover disease mechanisms and advance precision medicine. Her long-term research interests include developing computational methods to understand how genetic, molecular, and environmental factors jointly shape metabolic disease risk, disease heterogeneity, and progression. -
Donghoon Kim
Postdoctoral Scholar, Radiology
BioDr. Donghoon Kim is a postdoctoral scholar at Stanford’s Center for Advanced Functional Neuroimaging (CAFN), working in close collaboration with the Stanford Alzheimer’s Disease Research Center (ADRC). His work develops cutting-edge deep learning approaches for multimodal neuroimaging analysis, with an emphasis on the early detection and characterization of Alzheimer’s disease pathology.
Before joining Stanford, he earned his Ph.D. in Biomedical Engineering from the University of California, Davis. His Ph.D. thesis was titled "Deep Learning-Driven Technical Developments and Clinical Applications of Arterial Spin Labeling MRI." During his Ph.D. studies, he focused on the development of advanced deep learning techniques for ASL MRI and its clinical applications. During his master's degree in Biomedical Engineering at Virginia Tech–Wake Forest University, he studied the functional connectivity of the default mode network using resting-state BOLD fMRI among youth football players. -
Jiyeong Kim
Postdoctoral Scholar, Dermatology
BioDr. Jiyeong Kim is a post-doctoral scholar at the Stanford Center for Digital Health and the Department of Dermatology School of Medicine. As a multi-disciplined data scientist, Dr. Kim applies artificial intelligence (AI) to clinical informatics, harnessing patient-generated health information and data to enhance patient-centered care, which could be tailored to each patient group for improving patient engagement and better health outcomes. In her work, Dr. Kim leverages large language models, machine learning, and natural language processing to understand patients' and caregivers' genuine voices of care needs and needed support for individuals with chronic diseases, not limited to diabetes, cardiovascular disease, and cancer.
Research Interest
-LLMs and Generative AI to Listen to the Patient
-Generative AI-Assisted Enhanced Patient Care
-ML-based Disease Prediction Modeling
-Patient-Generated Data and Precision Health -
Minho Kim
Postdoctoral Scholar, Civil and Environmental Engineering
BioMinho Kim is a postdoctoral scholar at the Stanford Urban Resilience Initiative (SURI) within the Department of Civil and Environmental Engineering at Stanford University. His research focuses on natural hazard risk analysis and decision support systems.
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Samsuk Kim, PhD.
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioDr. Samsuk Kim is a dual research and clinical T32 fellow at Stanford University. She earned her PhD in Clinical Psychology from the University of Detroit Mercy and completed external research training at the University of Michigan (Kratz Lab), where she studied psychosocial factors—such as mindfulness and pain acceptance—in chronic pain. She also completed an APA-accredited internship at the VA Boston Healthcare System. Clinically, Dr. Kim specializes in pain management, health promotion, adjustment-related challenges, and emotional regulation. She draws from a range of evidence-based treatments, including Cognitive Behavioral Therapy (CBT), Acceptance and Commitment Therapy (ACT), mindfulness-based interventions, Dialectical Behavior Therapy (DBT), and interpersonal psychotherapy. Her current research focuses on understanding the bidirectional relationship between sleep and pain and developing personalized, digital interventions to improve outcomes in both domains.
