Welcome! I am a NSF postdoctoral fellow and von Karman instructor in Applied & Computational Mathematics at Caltech. I work on data-driven inference and control for large-scale dynamical systems, with a focus on optimal design for these outcomes. This includes optimizing sensor and actuator placements, measurement strategies, and sampling of spatial, temporal or parameter domains for inference and control of high-dimensional systems. Although these optimizations are in general NP-hard, my work exploits low-dimensional structure computed using dimensionality reduction and manifold learning to efficiently optimize design objectives. To learn more, click below.