About me

Christopher Lee

PhD candidate · Computer Science · University of South Carolina

AI/ML engineer and computer science PhD candidate at the University of South Carolina, applying machine learning to biomedical, public-health, and research data.

Education

  • PhD, Computer Science

    University of South Carolina

    Expected 2026

  • MS, Computer Science

    University of South Carolina

    2024

  • BS, Biology

    University of South Carolina

    2010

Experience

  • AI/ML Engineer

    Artificial Intelligence Institute, University of South Carolina

    • Design, build, and deploy production AI applications and research platforms for faculty and researchers.
    • Lead applied machine learning and LLM projects end to end, spanning natural language processing, computer vision, and automated literature review.
    • Architect secure, privacy-preserving AI infrastructure, including self-hosted large language models.

    2025 – present

  • Graduate Research Assistant

    University of South Carolina

    • Applied machine learning and deep learning to complex biomedical and population-health datasets.
    • Built predictive and classification models for bioinformatics, epidemiology, and neuroscience.
    • Worked with faculty, clinicians, and research teams to shape questions, design analyses, and interpret results.
    • Contributed to manuscripts, grant proposals, and conference presentations.

    2021 – 2025

  • Executive Director (started as Patient Coordinator)

    Columbia Oral Health Clinic

    • Led a federally funded nonprofit dental clinic: operations, budgeting, and grants.
    • Built partnerships with hospitals, providers, and community organizations to coordinate patient care.

    2012 – 2019

Skills

Technical Python · R · SQL · PyTorch · scikit-learn · pandas · NumPy · Jupyter · Git

Data & research Machine learning · Deep learning · Predictive modeling · Statistical analysis · Experimental design · Model evaluation · Data wrangling

Publications

  1. Geographic expansion, not viral intensification, leads to human dengue outbreaks in Mexico: a 40-year integrated remote sensing and machine learning analysis

    H Li, C Lee, ST Sweeney, H Valafar, MS NolanFrontiers in Public Health 14, 2026

  2. Reformulation of the Protein Data Bank for real-time search of geometrical attributes of protein structures

    M Azeem, C Lee, A Hein, C Ott, H ValafarFrontiers in Molecular Biosciences 12, 2026

  3. Filling the gap: establishing a statewide tick and tick-borne pathogen surveillance program

    KC Dye-Braumuller, L Gual-González, EO Pickle, C Lee, M Meyer, CL Evans, JG Chandler, RT Trout Fryxell, MS NolanInsects 17(4), 414, 2026

  4. Tcruz-fpn dataset: slide-level partitioned quadrant images for Trypanosoma cruzi classification from the Morais et al. smartphone microscopy corpus Dataset

    C LeeZenodo, 2026

  5. Evaluation of the 2022 West Nile virus forecasting challenge, USA

    RD Harp, KM Holcomb, … MSJ McCarter, C Lee, MS Nolan, … MA JohanssonParasites & Vectors 18, 152, 2025

  6. The utility of a Bayesian predictive model to forecast neuroinvasive West Nile virus disease in the United States of America, 2022

    MSJ McCarter, S Self, KC Dye-Braumuller, C Lee, H Li, MS NolanPLoS ONE 18(9), e0290873, 2023

  7. First report of multiple Rickettsia sp., Anaplasma sp., and Ehrlichia sp. in the San Miguel Department of El Salvador from zoonotic tick vectors

    KC Dye-Braumuller, MK Lynn, PM Cornejo Rivas, C Lee, MS Rodríguez Aquino, JG Chandler, RR Trout Fryxell, SCW Self, M Kanyangarara, MS NolanActa Tropica 242, 106909, 2023