Bio

Lucas is a student researcher who has worked with Nobel Laureates Michael Levitt (Stanford) and Eric Betzig (UC Berkeley), as well as Dr. Bingwei Lu (Stanford) and Dr. Xiling Shen (Terasaki Institute). He is a co-author on several publications and manuscripts, including a manuscript under review in Nature, and has presented his research at national and international conferences. His work integrates molecular biology with deep learning and computational modeling to advance understanding of neuroscience, cancer, and immunology.

Beyond the lab, Lucas is dedicated to fostering a supportive community of future scientists. He has volunteered with the Hiller Aviation Museum and Forging Opportunities for Refugees in America (FORA), and founded IRIS (Internships and Research for Inquisitive Students), an organization that expands access to STEM by connecting high school students with labs and mentors. For his community leadership and service, he was named a Coca-Cola Scholars Program Semifinalist and has advanced to the final rounds.

Current Role at Stanford

Lucas conducts research directly with Nobel Laureate Michael Levitt on AI/ML-related and computational biology topics.

Supervisors

Contact

  • Work
    lucasy47@stanford.edu
    University - Staff Department: Department of Structural Biology Position: High School Student
    University - Incoming student Department: Vice Provost for Undergraduate Education Position: Undergraduate

Additional Info

  • Mail Code: 5126

All Publications

  • AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond. Frontiers in immunology Chao, C. C., Chiu, Y., Yeung, L., Yee, C., Jiang, C., Shen, X. 2025; 16: 1651533

    Abstract

    T cells play a dual role in various physiopathological states, capable of eliminating tumors and infected cells, while also playing a pathogenic role when activated by autoantigens, causing self-tissue damage. The regulation of T cell-peptide/major histocompatibility complex (TCR-pMHC) recognition is crucial for maintaining disease balance and treating cancer, infections, and autoimmune diseases. Despite efforts, predictive models of TCR-pMHC specificity are still in the early stages. Inspired by advances in protein structure prediction via deep neural networks, we evaluated AlphaFold 3 (AF3)-based AI computation as a method to predict TCR epitope specificity. We demonstrate that AlphaFold can model TCR-pMHC interactions, distinguishing valid epitopes from invalid ones with increasing accuracy. Immunogenic epitopes can be identified for vaccine development through in silico high-throughput processes. Additionally, higher-affinity and specific T cells can be designed to enhance therapy efficacy and safety. An accurate TCR-pMHC prediction model is expected to greatly benefit T-cell-mediated immunotherapy and aid drug design. Overall, precise prediction of T-cell immunogenicity holds significant therapeutic potential, allowing the identification of peptide epitopes linked to tumors, infections, and autoimmune diseases. Although there is much work to be done before these predictions achieve widespread practical use, we are optimistic that deep learning-based structural modeling is a promising pathway for the generalizable prediction of TCR-pMHC interactions.

    View details for DOI 10.3389/fimmu.2025.1651533

    View details for PubMedID 40948755

    View details for PubMedCentralID PMC12426251

  • Impact of HLA loss on NK cell infiltration and resistance to immunotherapy Adefioye, O., Han, Y., Yeung, L., Shen, A., Wang, J., Wang, Z., Chao, C., Jiang, C. AMER ASSOC CANCER RESEARCH. 2025

    View details for DOI 10.1158/1538-7445.AM2025-7349

    View details for Web of Science ID 001500371006417

  • Distinct Tumor-Associated Macrophage Signatures Shape the Immune Microenvironment and Patient Prognosis in Renal Cell Carcinoma Cells Han, Y., Shen, A., Chao, C., Yeung, L., Shen, X., Jiang, C., et al 2025

    View details for DOI 10.3390/cells14211740