Stuart, Andrew M.

Committee Member — 25 thesises

• López Gómez, Ignacio — A Unified Data-Informed Model of Turbulence and Convection for Climate Prediction (2023, Dissertation (Ph.D.))
• da Silva, Andre Fernando de Castro — An EnKF-Based Flow State Estimator for Aerodynamic Problems (2019, Dissertation (Ph.D.))
• Zhang, Pengchuan — Compressing Positive Semidefinite Operators with Sparse/Localized Bases (2017, Dissertation (Ph.D.))
• Catanach, Thomas Anthony — Computational Methods for Bayesian Inference in Complex Systems (2017, Dissertation (Ph.D.))
• Soh, Yong Sheng — Fitting Convex Sets to Data: Algorithms and Applications (2019, Dissertation (Ph.D.))
• Darcy, Matthieu D. — Gaussian Processes for Scientific Machine Learning: PDE Solvers and Operator Learning (2027, Dissertation (Ph.D.))
• Budninskiy, Maxim A. — Geometry-Driven Model Reduction (2019, Dissertation (Ph.D.))
• Anderson, Thomas Geoffrey — Hybrid Frequency-Time Analysis and Numerical Methods for Time-Dependent Wave Propagation (2021, Dissertation (Ph.D.))
• Kusanovic, Danilo Smiljan — Improving Reduced Order Models of Soil-Structure Interaction Using an Ensemble Kalman Inversion Finite Element Model Updating Framework (2021, Dissertation (Ph.D.))
• Teegavarapu, Ritvik S. — Interacting Particle Systems for Sampling from Non-Gaussian Targets (2026, Senior thesis (Major))
• Burov, Dmitry Anatolyevich — Kernel Methods for Learning About Complex Dynamical Systems (2024, Dissertation (Ph.D.))
• Taeb, Armeen — Latent-Variable Modeling: Algorithms, Inference, and Applications (2020, Dissertation (Ph.D.))
• Yoo, Gene Ryan — Learning Patterns with Kernels and Learning Kernels from Patterns (2021, Dissertation (Ph.D.))
• Zhang, Ziyun — Low-Rank Matrix Recovery: Manifold Geometry and Global Convergence (2023, Dissertation (Ph.D.))
• Levine, Matthew Emanuel — Machine Learning and Data Assimilation for Blending Incomplete Models and Noisy Data (2023, Dissertation (Ph.D.))
• Kovachki, Nikola Borislavov — Machine Learning and Scientific Computing (2022, Dissertation (Ph.D.))
• Wihardja, Adeline Emily — Model Discovery in Mechanics: From Conventional to Data-Driven Methods (2027, Dissertation (Ph.D.))
• Chen, Yifan — On Multiscale and Statistical Numerical Methods for PDEs and Inverse Problems (2023, Dissertation (Ph.D.))
• Calvello, Edoardo — Operator Learning for Inference in Dynamical Systems (2026, Dissertation (Ph.D.))
• Trautner, Margaret Katherine — Operator Learning for Scientific Computing (2025, Dissertation (Ph.D.))
• Rosa-Raíces, Jorge Luis — Path Space Markov Chain Monte Carlo Methods for Molecular Simulation (2022, Dissertation (Ph.D.))
• Ocegueda, Eric — Physics-Based and Data-Driven Computational Models of Inelastic Deformations (2023, Dissertation (Ph.D.))
• Huang, De — Positive Definite Matrices: Compression, Decomposition, Eigensolver, and Concentration (2020, Dissertation (Ph.D.))
• Chen, Jiajie — Singularity Formation in Incompressible Fluids and Related Models (2022, Dissertation (Ph.D.))
• Nelsen, Nicholas Hao — Statistical Foundations of Operator Learning (2024, Dissertation (Ph.D.))