Curriculum Vitae

General Information

Full Name Wentao Li
Languages English, Chinese
Programming Python (Pytorch, Tensorflow), R, JavaScript, Plink, Docker, MongoDB
Skills Machine Learning, Deep Learning, Foundation Modeling, Genetic Imaging, Genomic Studies, Medical Imaging Studies, Federated Learning, Privacy-preserving AI

Education

  • 2021 - 2025
    PhD in Biomedical Informatics
    University of Texas Health Science Center at Houston (UTHealth), US
    • Dean’s Excellent Award 2021
    • Jingchun Sun Memorial Scholarship 2023
    • D.Bradley McWilliams Scholars Endowed Scholarship Award 2024
  • 2018 - 2020
    Master of Science in Statistics
    University of California, San Diego, US
  • 2014 - 2018
    Bachelor of Science in Mathematics
    Shanghai Maritime University, China
    • Dean’s List of SMU, 2016.
    • First Class Scholarship of SMU, 2017

Experience

  • 2023 - present
    Research Assistant
    The University of Texas MD Anderson Cancer Center, US
    • Developed a novel cross-modal attention fusion method to enhance predictive modeling in cancer studies.
    • Performed survival analysis on lung cancer patients in immunotherapy and chemotherapy.
    • Investigated brain regional interactions and genetic variant expressions with a novel approach that incorporates individualized brain patterns in genomic association analysis.
  • 2025
    Machine Learning Scientist Intern
    Tempus AI, US
    • Developed a cell-type prediction model with foundation models on Spatial Transcriptomic data (VisiumHD), which enhanced the accuracy of cell-type identification in cancer research.
  • 2020 - 2023
    Research Assistant
    The University of Texas Health Science Center at Houston, US
    • Developed COLLAGENE, a secure analysis tool offers a practical solution for privacy-preserving GWAS.
    • Developed series of Federated Generalized Linear Mixed Models (FedGLMMs) for Genome-Wide Association Studies (GWAS).
    • Developed a privacy-preserving federated learning method for cohort studies.
    • Created a deep learning model to accurately predict blood pressure from photoplethysmogram (PPG) signals.
    • Hosted federated training across Houston, San Diego, and Munich with VERTIGO-CI.
  • 2019 - 2020
    Research Assistant
    University of California San Diego, US
    • Conducted mathematical proofs for calibration measurements and models in clinical prediction research

Seminars & Speaches

  • 2025
    International Symposium on Biomedical Imaging 2025 (ISBI 2025)
    • Workshop host and committee member of the Glioma-MDC 2025 challenge – Detection and classification of mitotic cells in glioma from digital pathological images.
  • 2021
    AMIA 2021 Virtual Informatics Summit
    • Presentation on published conference paper "VERTIcal Grid lOgistic regression with Confidence Interval"
  • 2024
    MICCAI 2024 CMMCA Workshop
    • Presentation on published conference paper "Attention-fusion Model for Multi-Omics (AMMO) Data Integration in Lung Adenocarcinoma"

Research Interests

  • Medical imaging research
    • Multimodality imaging modeling (CT/PET/MRI)
    • Chest CT foundation model
  • Genetic imaging research
    • Spatial Transcriptomics
    • Cross-modal attention fusion in multi-omics data integration
  • Genome-Wide Association Studies (GWAS)
    • Generalized linear Mixed Model Association Tests
    • Kinship relationship estimation
  • Privacy-preserving machine learning
    • Federated Learning
    • Differential Privacy
    • Secure Multi-party computation
    • Homomorphic Encryption

Open Source Projects

  • 2022 - 2024
    Federated Learning Platform (FedPlatform) development
    • Developed a lightweight cross-silo federated learning platform based on the browser.
    • Embed a Python distribution on the browser to accomplish federated learning tasks. This lightweight system can free federated trainers from installing any dependencies.
    • Accomplished multi-party data collaboration simulation test on linear regression with federated learning.
    • Ongoing project aims to bridge isolated data islands and provide an experience-friendly platform for non-professional users to collaborate on federated learning tasks.
  • 2022 - present
    FedML MLOpsCloud-Web development
    • Developed a web-based cross-silo federated learning feature in FedML.
    • Designed and deployed a generalised framework in web-based federated learning, which aligns model structures during communication between browsers (Tensorflow.js) and the server (Pytorch).

Other Interests

  • Hobbies: Hikings, BBQ, etc.