Todd Michael

Todd Michael is an Adjunct Professor at the University of California, San Diego, affiliated with the Scripps Institution of Oceanography and the Center for Marine Biotechnology and Biomedicine. His work bridges advanced computational methods with structural engineering challenges, particularly in infrastructure monitoring and material degradation analysis.

Research interests include Structural Health Monitoring (SHM), Non-Destructive Evaluation (NDE), and the integration of Machine Learning and Bayesian Inference into engineering systems. He focuses on optimizing sensor networks, improving corrosion diagnostics for large structures like miter gates, and developing digital twin frameworks for predictive maintenance. Recent work emphasizes probabilistic modeling, anomaly detection under complex conditions, and the application of deep learning to civil and mechanical engineering problems.

His articles highlight innovations in SHM strategies for bridges and aircraft components, sensor reliability under operational failures, and frameworks for cost-effective monitoring. He also explores automated crack detection in underwater environments and the use of physics-constrained neural networks to model structural dynamics accurately.

Michael collaborates with interdisciplinary teams on projects involving smart infrastructure and AI-driven inspection technologies. While no formal awards are listed, his contributions reflect a strong focus on applied research with real-world engineering implications.