Publications

Recent Preprints and Papers

Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and …
(2024). Computation-Aware Kalman Filtering and Smoothing. arXiv preprint.
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Gaussian process (GP) hyperparameter optimization requires repeatedly solving linear systems with n×n kernel matrices. To address the …
(2024). Large-Scale Gaussian Processes via Alternating Projection. AISTATS.
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Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, …
(2023). Accelerating Generalized Linear Models by Trading Off Computation for Uncertainty. arXiv preprint.
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The neural tangent kernel (NTK) has garnered significant attention as a theoretical framework for describing the behavior of …
(2023). On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective. arXiv preprint.
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Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical …
(2022). Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. arXiv preprint.
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Talks

Recent and Upcoming

Apr 8, 2024 09:00 Southampton, UK
Jul 19, 2023 12:00 Cambridge, UK
Jun 27, 2023 11:10 Würzburg, Germany
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Teaching

Classes and Tutorials

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Projects

Software, Tech Reports, etc.

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

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