CV

Full curriculum vitae, including publications, talks, teaching, service, and awards.

Summary

  • Machine learning researcher working on generative modeling and inverse problems, with a focus on generation under physical constraints and in data-scarce regimes. My methods pair data-driven generative priors with hard consistency to known physics — developed and validated on medical imaging, but built on problem structure that transfers across scientific domains. I build the high-performance training and tooling around my models rather than handing off prototypes.

Education

  • 2025
    Ph.D., Computer Science
    Johns Hopkins University
    • Thesis advisor: Jerry L. Prince
  • 2023
    M.S.E., Computer Science
    Johns Hopkins University
  • 2019
    B.S., Computer Science
    Middle Tennessee State University
    • Summa Cum Laude, GPA 4.0

Experience

  • 2025 - Present
    Postdoctoral Researcher
    Johns Hopkins University
  • 2026
    Visiting Research Scientist
    Nara Institute of Science and Technology, Japan
  • 2020 - 2025
    PhD Student, NSF Fellow & Research Assistant
    Johns Hopkins University
  • 2017 - 2020
    Research Assistant
    Vanderbilt University
  • 2017 - 2020
    Special Volunteer / Research Assistant
    National Institutes of Health Clinical Center

Technical Skills

  • Machine learning
    • PyTorch; diffusion, flow, and generative models; generative priors
    • Self-supervised and zero-shot learning
    • Physics-informed and inverse-problem methods; classical ML
  • Numerical & signal methods
    • Inverse problems, optimization, sampling
    • Range/null-space decompositions, signal processing
  • Programming & compute
    • Python; GPU and distributed high-performance computing
    • Agentic software engineering; Git, Bash, LaTeX
    • Reproducible research tooling
  • Application domains
    • Reconstruction, super-resolution, denoising, registration, segmentation
    • MRI physics; neuroimaging pipelines

Selected Honors and Awards

  • 2025
    • IPMI Best Poster Award (Cycle-Consistent Zero-Shot Through-Plane Super-Resolution)
  • 2023
    • SASHIMI Best Paper Award (Self-Supervised Super-Resolution)
  • 2020 - 2025
    • NSF Graduate Research Fellowship
  • 2017
    • Best Presentation of Research in Imaging, NIH Clinical Center
    • Best Poster Award (machine learning for brain MRI), NIH Clinical Center