Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets. - Hai Nguyen 002; Matthias Katzfuss; Noel Cressie; Amy Braverman (Technometrics, 2014)

Parallel inference for massive distributed spatial data using low-rank models. - Matthias Katzfuss; Dorit Hammerling (Stat. Comput., 2017)

Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data. - Daniel Zilber; Matthias Katzfuss (Comput. Stat. Data Anal., 2021)

Multi-Resolution Filters for Massive Spatio-Temporal Data. - Marcin Jurek; Matthias Katzfuss (J. Comput. Graph. Stat., 2021)

Spatial Retrievals of Atmospheric Carbon Dioxide from Satellite Observations. - Jonathan Hobbs; Matthias Katzfuss; Daniel Zilber; Jenný Brynjarsdóttir; Anirban Mondal; Veronica J. Berrocal (Remote. Sens., 2021)

Sparse Cholesky Factorization by Kullback-Leibler Minimization. - Florian Schäfer 001; Matthias Katzfuss; Houman Owhadi (SIAM J. Sci. Comput., 2021)

Scalable Gaussian-process regression and variable selection using Vecchia approximations. - Jian Cao 004; Joseph Guinness; Marc G. Genton; Matthias Katzfuss (J. Mach. Learn. Res., 2022)

Scaled Vecchia Approximation for Fast Computer-Model Emulation. - Matthias Katzfuss; Joseph Guinness; Earl Lawrence (SIAM/ASA J. Uncertain. Quantification, 2022)

Spatial Surface Reflectance Retrievals for Visible/Shortwave Infrared Remote Sensing via Gaussian Process Priors. - Daniel Zilber; David R. Thompson 001; Matthias Katzfuss; Vijay Natraj; Jonathan Hobbs; Amy Braverman (Remote. Sens., 2022)

Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering. - Marcin Jurek; Matthias Katzfuss (Stat. Comput., 2022)

Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. - Felix Jimenez 002; Matthias Katzfuss (AISTATS, 2023)

Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization. - Jian Cao 004; Myeongjong Kang; Felix Jimenez 002; Huiyan Sang; Florian Tobias Schaefer; Matthias Katzfuss (ICML, 2023)

Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization. - Jian Cao 004; Myeongjong Kang; Felix Jimenez 002; Huiyan Sang; Florian Schäfer 001; Matthias Katzfuss (CoRR, 2023)

Sparse Cholesky factorization by greedy conditional selection. - Stephen Huan; Joseph Guinness; Matthias Katzfuss; Houman Owhadi; Florian Schäfer 001 (CoRR, 2023)

Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference. - Myeongjong Kang; Matthias Katzfuss (Stat. Comput., 2023)

Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks. - Felix Jimenez 002; Matthias Katzfuss (AISTATS, 2025)

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks. - Felix Jimenez 002; Matthias Katzfuss (CoRR, 2025)