Summer 2025

Development of a joint optimization framework that simultaneously reconstructs X-ray ptychography and fluorescence data, leveraging their complementarity for improved image reconstruction compared to separate methods.

This project investigates hybrid LSLU, a novel iterative regularization method for training random feature models that avoids the double descent phenomenon while requiring fewer iterations and less computational cost than existing approaches like hybrid LSQR.

Developed a complete pipeline for optimal control of predator-prey systems under uncertainty. Combined Bayesian parameter estimation with MCMC, pseudo-spectral methods, and Pontryagin’s Maximum Principle to propagate uncertainty through control decisions for Lotka-Volterra dynamics.

Exploring linear encoder-decoder networks for interpretable inverse problem solutions using Bayes risk minimization.