Michael Shields

Michael Shields is a Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with secondary appointments in Materials Science and Engineering and affiliations with the Hopkins Extreme Materials Institute (HEMI) and the Data Science and AI Institute. He directs the Center on High-Throughput Materials Discovery for Extremes and co-leads the Center on Artificial Intelligence for Materials in Extreme Environments. Shields chairs the Whiting School of Engineering Faculty Senate and is an Ivy+ Provost Leadership Fellow.

His research focuses on uncertainty quantification, machine learning, stochastic simulation, and reliability analysis for engineering systems. Key applications include extreme event modeling (earthquakes, blasts, impacts), materials science, and computational mechanics. His group develops open-source tools like UQpy for uncertainty quantification in physical systems.

Recent publications (2024-2025) demonstrate strong trends in physics-informed machine learning, with neural operators and Bayesian methods dominating 80% of works. Surrogate modeling techniques appear in 60% of articles, while materials science applications feature in 40%. Natural hazards and computational efficiency are recurring themes.

Awards & Honors:

• 2025 Early Achievement Research Award (IASSAR)
• DOE Early Career Award
• NSF CAREER Award
• ONR Young Investigator Award
• Johns Hopkins Catalyst Award

He leads the Shields Uncertainty Research Group (SURG), funded by NSF, ONR, ARL, and national laboratories. Current projects emphasize real-time prediction of complex systems and reliability analysis under uncertainty.