Andrew Stuart
Bren Professor of Computing and Mathematical Sciences
B.S., University of Bristol, 1983; Ph.D., University of Oxford, 1986. Bren Professor, Caltech, 2016-.
Research interests: data assimilation, inverse problems, stochastic modeling
Overview
Professor Stuart's research is focused on the development of mathematical and algorithmic frameworks for the seamless integration of models with data. He works in the Bayesian formulation of inverse problems, and in data assimilation for dynamical systems. Quantification of uncertainty plays a significant role in this work. Current applications of interest include a variety of problems in the geophysical sciences, and in graph-based learning.
Related News
Read more newsPublications
- Kaveh, Hojjat;Avouac, Jean-Philippe et al. (2025) Spatiotemporal forecast of extreme events in a chaotic model of slow slip eventsGeophysical Journal International
- Batlle, Pau;Chen, Yifan et al. (2025) Error analysis of kernel/GP methods for nonlinear and parametric PDEsJournal of Computational Physics
- Pradhan, Anshuman;Adams, Kyra H. et al. (2024) Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network RegressionJournal of Geophysical Research: Machine Learning and Computation
- Carrillo, J. A.;Hoffmann, F. et al. (2024) The Mean-Field Ensemble Kalman Filter: Near-Gaussian SettingSIAM Journal on Numerical Analysis
- Wu, Jin-Long;Levine, Matthew E. et al. (2024) Learning about structural errors in models of complex dynamical systemsJournal of Computational Physics
- Bach, Eviatar;Colonius, Tim et al. (2024) Filtering dynamical systems using observations of statisticsChaos: An Interdisciplinary Journal of Nonlinear Science
- Schneider, Tapio;Behera, Swadhin et al. (2023) Harnessing AI and computing to advance climate modelling and predictionNature Climate Change
- Sirlanci, Melike;Levine, Matthew E. et al. (2023) A simple modeling framework for prediction in the human glucose–insulin systemChaos: An Interdisciplinary Journal of Nonlinear Science
- de Hoop, Maarten V.;Kovachki, Nikola B. et al. (2023) Convergence Rates for Learning Linear Operators from Noisy DataSIAM/ASA Journal on Uncertainty Quantification
- Bhattacharya, Kaushik;Liu, Burigede et al. (2023) Learning Markovian Homogenized Models in ViscoelasticityMultiscale Modeling & Simulation
Related Courses
2023-24
CMS/ACM/IDS 107 ab – Linear Analysis with Applications
2022-23
CMS/ACM/IDS 107 – Linear Analysis with Applications
ACM 109 – Mathematical Modelling
ACM/IDS 154 – Inverse Problems and Data Assimilation
ACM/IDS 180 ab – Multiscale Modeling
2021-22
CMS/ACM/IDS 107 – Linear Analysis with Applications
ACM/IDS 154 – Inverse Problems and Data Assimilation
2020-21
CMS/ACM/IDS 107 – Linear Analysis with Applications
ACM 109 – Mathematical Modelling