Michael Todd

About

Michael Todd is a Professor at the University of California San Diego, specializing in structural health monitoring and anomaly detection in mechanical systems. His recent research focuses on hierarchical Bayesian approaches, robust vision-based methods for damage detection, and the application of deep learning in structural analysis. He has published extensively on topics such as corrosion diagnostics and the optimal design of sensor placement for monitoring infrastructure under varying conditions, contributing significantly to the field of engineering and applied sciences.