A Geometry-Aware Data-Driven Framework for Predictive Modeling and PDE Operators

Explainable Radio-Pathomic Model for Classifying Molecular Subtypes of Adult Gliomas

Comparing Manifold-Constrained Gaussian Processes and Physics-Inspired Neural Networks for Parameter Inference in Epidemiological Models

Compression of Tensor Train Cores via Tucker Decomposition, and Applications to the Density Matrix Renormalization Group

On the Choice of Subspace for the Quasi-minimal Residual Method for Linear Inverse Problems

Pixels, Priors, and Precision: Bayesian Approaches to Image Reconstruction

Inexact Generalized Golub-Kahan Methods for Large-Scale Inverse Problems

Inner Product Free Krylov Methods for Inverse Problems

Robust Uncertainty Quantification for Foundation Models: Bayesian and Frequentist Approaches for High-Stakes Applications