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Education

  • Ph.D. in Applied Mathematics
    Yale University
    • Advised by Ronald Coifman and Yuval Kluger.
  • M.S. in Applied Mathematics
    Yale University
  • B.S. in Computer Science and Mathematics
    Yale University

Experience

  • 2024
    Machine Learning Research Intern
    ByteDance
    • Probing the capabilities of diffusion-based early-fusion multi-modal generative models on the AI Seed-Vision Team. Mentors - Peng Wang and Linjie Yang.
  • 2024
    Machine Learning Research Intern
    Elucid
    • Investigated the usage of multimodal foundation models to aid in generating and augmenting arterial CT imagery and segmentations for improved fractional flow reserve (FFR) analysis and cardiologist report generation.
  • 2023
    Machine Learning Research Intern
    Bosch Center for Artificial Intelligence
    • Conducted research on robust training-free approaches to guided diffusion models using optimal control. Published at NeurIPS 2024. Mentor - Marcus Pereira.
  • 2020
    Machine Learning Research Intern
    Center for Computational Mathematics, Flatiron Institute
    • Explored deep image prior-based techniques for enhancing phase retrieval in low-photon settings at the Center for Computational Mathematics (CCM) at Flatiron Institute
  • 2016
    Software Engineering Intern
    Amazon Lab126
    • Modified machine learning module (an n-gram Markov model) in FireOS to reduce memory usage by ~2x with no significant reduction in prediction quality