Stanford University


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  • Allen Wu

    Allen Wu

    Principal Engineer At Eli Lilly And Company, Medicine - Med/Cardiovascular Medicine

    BioAllen Wu is a machine learning engineer and researcher specializing in large-scale AI/ML infrastructure for biomedical and pharmaceutical applications. He contributed to the development of TuneLab, an Eli Lilly AI/ML platform supporting federated drug discovery with external biotech partners, and Model Gateway, an MLOps inference platform for internal model-driven drug discovery, during his tenure at Eli Lilly. He holds dual MS degrees in Computer Science (National Chiao Tung University, Taiwan) and Software Engineering (San José State University), with publications in both VLSI/EDA and Pharma AI, and is an IEEE peer reviewer and NSTC LEAP Fellow (hosted at Synopsys). His current research interests focus on the intersection of iPSC biology and AI/ML pipelines for precision medicine.

    LinkedIn Profile: https://www.linkedin.com/in/yswu123
    Google Scholar Profile: https://scholar.google.com/citations?user=ymttL5gAAAAJ

  • Yingcheng Wu

    Yingcheng Wu

    Postdoctoral Scholar, Pathology

    BioYingcheng (Charles) Wu is an AI4S postdoc advised by Le Cong and Mengdi Wang, focusing on physical AI scientists, biomedical world models, and autonomous science infra. His work combines embodied AI, world model, and agents to build self-driving laboratories. He has published studies in **Cell**, **Science**, **Nature**, and received honors including the ICIS Sidney & Joan Pestka Award, the IUBMB Young Scientist Fellowship, the APASL Young Investigator Award, World’s Top 2% Scientists by Stanford, WAIC Rising Star Award.

  • Yue Wu

    Yue Wu

    Postdoctoral Scholar, Genetics

    Current Research and Scholarly InterestsI built computational methods to integrate and model biological time series, including metabolic dynamics, longitudinal multi-omics data, and micro-sampling. I reduce dimensions, built clusters, and search for causal links.

  • Ziyan Wu

    Ziyan Wu

    Postdoctoral Scholar, Otolaryngology - Head & Neck Surgery

    BioZiyan’s PhD research centers around contaminant sensing in the environment using Raman spectroscopy, membrane sensors, and machine learning. At Stanford, Ziyan will continue her research on investigating the health impacts of emerging contaminants.

  • Wanat Wudhikulprapan

    Wanat Wudhikulprapan

    Affiliate, Rad/Musculoskeletal Imaging

    BioDr. Wanat Wudhikulprapan is an attending radiologist and instructor/ lecturer in the Department of Radiology at Chiang Mai University, Chiang Mai, Thailand. He earned his MD from Chulalongkorn University, completed diagnostic radiology residency training (2022), and pursued a research fellowship in musculoskeletal imaging at the Department of Radiology, Stanford University (2025–2026). His clinical and research interests center on musculoskeletal MRI, with a focus on dynamic and functional imaging techniques, quantitative assessment, bone marrow disease and MSK infection, and deep learning–enabled image analysis to advance diagnostic precision.

    Dr. Wudhikulprapan is a member of the Royal College of Radiologists of Thailand (RCRT), ISMRM, and International Skeletal Society

    Education

    Research Fellowship: Musculoskeletal Imaging — Stanford University, Department of Radiology, Stanford, CA (2025–2026)
    Clinical fellowship: Body Imaging — Chiang Mai University (class of 2022)
    Residency: Diagnostic Radiology — Ramathibodi Hospital ,Mahidol University (class of 2020)
    Medical education (MD): Chulalongkorn University, Bangkok, 2016

    Publications
    - Patellofemoral instability: Anatomy, imaging techniques, and clinically relevant measurements
    - Diagnosing osteomyelitis in diabetic foot by diffusion-weighted imaging and dynamic contrast material-enhanced magnetic resonance imaging: a systematic review and meta-analysis
    - Iron overload and programmed bone marrow cell death: Potential mechanistic insights
    - Can conventional magnetic resonance imaging at presentation predict chemoresistance in osteosarcoma?