CV

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Contact Information

Name Eddie Cunningham
Professional Title PhD Candidate

Professional Summary

PhD candidate at UMass Amherst researching the intersection of differential geometry, generative modeling, and representation learning.

Experience

  • Sep 2019 - Present

    Amherst, MA, USA

    Graduate Research Assistant
    University of Massachusetts, Information Fusion Lab
    • Published papers at top conferences on normalizing flows and dimensionality reduction
    • Implemented all research from scratch in JAX, building three open-source libraries for diffusion/flow based generative models, stochastic processes, and Riemannian geometry
    • Applied methods across problem domains including self-supervised representation learning, time series generation, and density estimation on manifolds
  • Jun 2021 - Aug 2021

    Menlo Park, CA, USA

    Research Intern
    SRI International, Computer Science Laboratory
    • Led research project on finding principal structure in normalizing flow models
    • Developed theory and algorithms to extract low-dimensional structure from normalizing flow models
    • Intern project resulted in paper accepted at ICML 2022
  • Jul 2017 - May 2019

    Olathe, KS, USA

    Sensors and Algorithms Team / GPS Positioning
    Garmin
    • Used sensor measurements from wrist mounted watch to develop Bayesian machine learning models
    • Developed real-time routing and positioning algorithm to build optimal race pace segments
    • Built Python testing framework for C++ algorithms, drastically reducing algorithm development time
  • May 2016 - Aug 2016

    Cupertino, CA, USA

    Software Engineering Intern
    Apple, Apple Watch Team
    • Developed automated testing framework for Apple Watch software, reducing manual QA effort
    • Implemented features in Objective-C, Swift, and Python for the Apple Watch software platform

Education

  • 2019 - 2026

    Amherst, MA, USA

    M.S./Ph.D.
    University of Massachusetts Amherst
    Computer Science
    • Thesis: Orthogonal coordinates for representation learning
    • Advisors: Daniel Sheldon and Madalina Fiterau
  • 2013 - 2016

    Ann Arbor, MI, USA

    B.S.E.
    University of Michigan
    Computer Science with Math Minor

Software

Research Areas

Generative modeling: Normalizing flows, continuous normalizing flows, flow matching, diffusion models
Stochastic processes: Linear SDEs, state space models, stochastic optimal control, Doob h-transforms
Differential geometry: Riemannian metrics, curvature, coordinate systems, pullback geometry
Optimal transport: Wasserstein distances, transport maps, flow matching
Representation learning: Self-supervised learning, dimensionality reduction, manifold learning, independent component analysis
Variational inference: ELBO, variational autoencoders, amortized inference
Sampling methods: Boltzmann sampling, MCMC, score-based methods
Time series modeling: Autoregressive models, state space models, diffusion-based time series

Publications

Awards

  • 2019
    Spaulding-Smith Fellowship
    University of Massachusetts Amherst

Skills

Research (): Normalizing flows, independent component analysis, diffusion models, flow matching, stochastic optimal control, optimal transport, variational inference, Boltzmann sampling, differential geometry, time series modeling, self-supervised representation learning
Programming (): Python, JAX, PyTorch, NumPy, C++

Languages

English : Native