Luhuan Wu

New York, NY


Hi, I am a 4th-year PhD student in Statistics at Columbia University, where I am very fortunate to work with Prof. John Cunningham. and Prof. David Blei. During my PhD, I’ve interned at Apple MLR hosted by Sinead Williamson. Previously, I received my master’s degree in Data Science at Columbia University, supervised by Prof. John Cunningham and Prof. Itsik Pe’er. I received my bachelor’s degree in Mathematics at Nanjing University.

Research Interests: In general I am interested in problems that invovle randomness and uncertainty, mostly through a probabilistic lens. Specifically, my recent focus has been:

  • Probabilistic modeling: deep generative models (e.g. diffusion models, VAE, deep state space models), and Bayesian inference (e.g. variational inference, SMC)
  • Uncertainty quantification: I’ve been mostly exploring the Bayesian approach, but I’m also open to other approaches
  • Invariant prediction and out-of-distribution generalization

Selected publications

  1. Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
    Luhuan Wu Wu, Brian Trippe, Christian Naesseth, David Blei, and John Cunningham
    In Conference on Neural Information Processing Systems 2023
  2. Variational Nearest Neighbor Gaussian Process
    Luhuan Wu, Geoff Pleiss, and John Cunningham
    In International Conference on Machine Learning 2022
  3. Bias-free Scalable Gaussian Processes via Randomized Truncations
    Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, and John P Cunningham
    In International Conference on Machine Learning 2021
  4. Hierarchical Inducing Point Gaussian Process for Inter-domian Observations
    Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David Blei, and John Cunningham
    In International Conference on Artificial Intelligence and Statistics 2021