Dimitris Giovanis is an assistant research professor in the Department of Civil and Systems Engineering. He is a fellow of the Hopkins Extreme Materials Institute, and a member of the Institute for Data Intensive Engineering and Science. He is also a member of the NSF’s NHERI Simulation Center (SimCenter).
His research focuses on foundational methodological research on manifold and machine learning, probabilistic modeling, and uncertainty quantification to advance risk-informed artificial intelligence-based predictive modeling that will transform the way scientists and engineers perform computer-assisted modeling of complex multiscale (and multiphysics) systems.
Giovanis’s current projects include investigating the influence of microstructural uncertainties and imperfections on the macroscopic properties of metallic glasses and energetic materials for advanced applications under extreme conditions, improving the fundamental understanding of post-wildfire debris flow and earthquake for risk assessment and management, quantifying the risk of traumatic brain injury in humans, quantifying uncertainty in the reliability-based analysis of engineering systems in the presence of small data, and exploring the influence of uncertainties in Chemical Vapor Deposition (CVD) reactors used to produce hard coatings and protective wear.
He earned his five-year diploma in civil (structural) engineering, his master’s in computational mechanics, and his doctorate in civil engineering—all from the National Technical University of Athens in Greece. Giovanis is a member of the ASCE/EMI Probabilistic Methods Committee, a member of the SIAM Activity Group on Uncertainty Quantification, a member of the Greek Association of Computational Mechanics, and a licensed Professional Civil Engineer in Greece.
He is also an assistant coach of the men’s water polo team at JHU.
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$4.8 million grant promotes research on scientific machine learning focused on greater predictive capabilities.