Publications

Recent Preprints and Papers

Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, …

Gaussian process (GP) hyperparameter optimization requires repeatedly solving linear systems with n×n kernel matrices. To address the …

The infinite-width limit of neural networks (NNs) has garnered significant attention as a theoretical framework for analyzing the …

Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical …

Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which …

Talks

Recent and Upcoming

Projects

Software, Tech Reports, etc.

Probabilistic Numerics (PN) interprets classic numerical routines as inference procedures by taking a probabilistic viewpoint. This …

In this project we analyzed time-optimal control problems with linear dynamics and numerical methods for solving them. When …

Contact

  • Columbia University
    Zuckerman Institute
    3227 Broadway
    New York, NY 10027
  • Office hours by appointment