School of Medicine
Showing 12,101-12,150 of 13,096 Results
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Sophia Y. Wang, MD, MS
Assistant Professor of Ophthalmology
Current Research and Scholarly InterestsI use and integrate a wide variety of data sources in my research, spanning both structured and unstructured forms, including national survey datasets, health insurance claims data, patient generated online text, surgical video, and electronic health records. I investigate outcomes of treatments for glaucoma and cataract, as well as other areas of ophthalmology. My focus is on developing artificial intelligence methods to predict ophthalmology outcomes, while ensuring fairness.
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Sui Wang, PhD
Associate Professor of Ophthalmology
Current Research and Scholarly InterestsOur research focuses on unraveling the molecular mechanisms underlying retinal development and diseases. We employ genetic and genomic tools to explore how various retinal cell types, including neurons, glia, and the vasculature, respond to developmental cues and disease insults at the epigenomic and transcriptional levels. In addition, we investigate their interactions and collective contributions to maintain retinal integrity.
1. Investigating retinal development:
We utilize genetic tools and methods such as in vivo plasmid electroporation and CRISPR to dissect the roles of cis-regulatory elements and transcription factors in controlling retinal development.
2. Understanding diabetes-induced cell-type-specific responses in the retina:
Diabetes triggers a range of multicellular responses in the retina, such as vascular lesions, glial dysfunction, and neurodegeneration, all of which contribute to retinopathy. We delve into the detailed molecular mechanisms underlying these diabetes-induced cell-type-specific responses and the pathogenesis of diabetic retinopathy.
3. Developing molecular tools for labeling and manipulation of specific cell types in vivo:
Cis-regulatory elements, particularly enhancers, play pivotal roles in directing tissue- and cell-type-specific expression. Our interest lies in identifying enhancers that can drive cell type-specific expression in the retina and brain. We incorporate these enhancers into plasmid or AAV-based delivery systems, enabling precise labeling and manipulation of specific cell types in vivo. -
Taia T. Wang, MD, PhD, MSCI
Associate Professor of Medicine (Infectious Diseases) and of Microbiology and Immunology
Current Research and Scholarly InterestsLaboratory of Mechanisms in Human Immunity and Disease Pathogenesis
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Tao Wang (王韬)
Director of Precision Diabetes Care, Genetics
Current Role at StanfordPrincipal Investigator, AI for Precision Diabetes Management
Project Manager & Scientific Co-lead, PsychENCODE Project
Project Initiator & Clinical Co-lead, Long COVID Clinical RCT with TCM
Project Initiator & Manager, AI & Wearables Toolkit for Biomedical Sciences
ENCODE and PsychENCODE Project Data Manager
Research Scientist, US Veteran Affairs Hospital
SCGPM HPC System Administrator -
Tong Wang
Instructor, Pathology
BioTong Wang, MD, PhD, is a physician-scientist in clinical pathology with interests in nucleic acid chemical biology, epigenetics, deep learning, and clinically useful tests.
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Tony Wang
Clinical Assistant Professor, Anesthesiology, Perioperative and Pain Medicine
BioTony Wang is a Clinical Assistant Professor in the Department of Anesthesiology, Perioperative, and Pain Medicine at Stanford University School of Medicine. He is a board-certified anesthesiologist and intensivist with clinical expertise in critical care medicine and liver transplant anesthesiology.
Dr. Wang completed his medical school training, anesthesiology residency, and critical care fellowship at the Johns Hopkins School of Medicine. Dr. Wang is deeply committed to medical education and academic innovation. He serves as the Associate Program Director for the Liver Transplant Anesthesiology Fellowship and is the founding director of Stanford's Anesthesiology Critical Care Education for Leaders (ACCEL) program. He is also Director of Residency Engagement for WikiAnesthesia, a comprehensive digital platform designed to serve as an open-access repository of anesthesia knowledge for trainees and practitioners.
Prior to medicine, Dr. Wang worked as a software engineer at Epic Systems, where he developed an appreciation for the intersection of technology and healthcare delivery.
His clinical interests include the perioperative management of complex patients, liver transplant anesthesiology, critical care medicine, and the integration of technology into anesthesiology education. -
Wenjun Wang
Postdoctoral Scholar, Stem Cell Transplantation
Current Research and Scholarly InterestsMy postdoctoral research focuses on investigating novel therapy for childhood leukemias.
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Wenmin Wang
Postdoctoral Scholar, Ophthalmology
Current Research and Scholarly InterestsI am particularly interested in identifying therapeutic strategies for various eye disorders and investigating the mechanisms by which defects in inositol phosphatases lead to the disruption of primary cilia function and eye diseases by using Omics.
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Xinnan Wang
Professor of Neurosurgery
Current Research and Scholarly InterestsMechanisms underlying mitochondrial dynamics and function, and their implications in neurological disorders.
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Tauska Lan
Affiliate, Genetics - BASE
BioI'm an ML engineer specializing in LLM post-training and agentic systems—with a particular focus on domains where rigor matters: health, biology, and scientific discovery.
Long-horizon agents — Designed and shipped multi-step orchestration systems (Pantheon-CLI, OmicVerse Agent) that outperform general SWE-agent baselines on biomedical tasks. Built cross-provider query routing and sandboxed execution to keep complex workflows robust over extended interactions. My agents don't just respond—they plan, recover from failure, and complete real research pipelines end-to-end.
Agentic science — Created infrastructure where AI doesn't assist research—it conducts it. Vectorized 30 years of NHANES data; parallelized Bayesian kernel machine regression on Kubernetes; built TCGA/GEO pipelines that bridge wet-lab and dry-lab workflows. Co-developed OmicVerse, an open-source platform powering reproducible multi-omics and single-cell analyses across hundreds of studies.
Experience engineering — Scaled rubric-based reward datasets to 1M+ pairs; trained summary and chain-of-thought reward models via RLAIF/RLHF; delivered measurable benchmark lifts in health AI. I care about the full loop: data curation → reward shaping → careful ablation → verifiable outcome—no cherry-picked demos—just metrics that survive scrutiny.
Currently pursuing advanced agentic studies at Karolinska Institutet and Stanford!
Open-source: OmicVerse · Pantheon-CLI · RAG Web UI · AstrBot
If you're working on post-training at scale, scientific agents, or high-integrity data pipelines—I'm always interested in systems that move from promising results to verifiable outcomes. Let's talk. -
Xunda Wang
Basic Life Res Scientist, Neurosurgery
Current Role at StanfordResearch Scientist
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Yidan Wang
Postdoctoral Scholar, Neonatal and Developmental Medicine
BioI joined Dr. John Benjamin’s lab to study the heterogeneity of neutrophils across developmental stages and during bronchopulmonary dysplasia (BPD). My research focuses on characterizing neutrophil subsets and investigating the contributions of distinct neutrophil subsets to the development of BPD.
My predoctoral training was completed at Northwestern University under the co-mentorship of Dr. Harris Perlman and Dr. Deborah Winter. I studied the heterogeneity and functions of monocyte-lineage cells and macrophages in mouse synovial tissue under both steady-state and inflammatory arthritis conditions. -
Yiyu Wang
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioDr. Yiyu Wang is a T32 postdoctoral researcher at the Department of Anesthesiology, Stanford School of Medicine. Her research combines computational models and neuroimaging techniques to characterize the neural architecture underlying complex human experiences in emotion and pain. Her current work focuses on leveraging deep learning, foundation models, and explainable AI to improve neuroimaging-based markers as well as multi-modal markers of chronic pain.
