Manohar Lab

Our group develops mathematical and computational algorithms for data-driven modeling, sparse sensing, and system identification in high-dimensional dynamical systems. Drawing on physics-informed structure, statistical mechanics and spectral methods, we establish performance guarantees, interpretability, and uncertainty quantification for learned models.

These developments enable machine learning in safety-critical aerospace and nuclear systems, where interpretability and robustness are essential. We design methods for reliable state estimation, sensor placement and scheduling, and closed-loop prediction and control under uncertainty.