Omid Moradian is an associate professor of Geomechanics in the Mining Engineering Program, Department of Metallurgical, Materials and Biomedical Engineering at UTEP. He earned his Ph.D. in Civil Engineering from Université de Sherbrooke in Canada and has completed postdoctoral research at Université de Sherbrooke and the Massachusetts Institute of Technology (MIT). Prior to joining UTEP, Dr. Moradian held academic and research appointments at New Mexico Tech, Colorado School of Mines, and the Swiss Federal Institute of Technology (ETH Zurich). By combining acoustic emission, ultrasonic monitoring, fiber-optic sensing, full-field digital image correlation, numerical modeling, and artificial intelligence, his laboratory investigates geomaterial behavior across spatial and temporal scales, from grain-scale deformation and distributed microcracking to damage localization, fracture coalescence, and macroscopic failure. This integrated framework seeks not merely to observe how geomaterials fail, but to uncover the governing physics of failure, understand how instability emerges from interacting processes, and ultimately develop physics-informed AI models for detecting precursors and forecasting failure in complex geological and engineered systems.
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Omid Moradian is an associate professor of Geomechanics in the Mining Engineering Program, Department of Metallurgical, Materials and Biomedical Engineering at UTEP. He earned his Ph.D. in Civil Engineering from Université de Sherbrooke in Canada and has completed postdoctoral research at Université de Sherbrooke and the Massachusetts Institute of Technology (MIT). Prior to joining UTEP, Dr. Moradian held academic and research appointments at New Mexico Tech, Colorado School of Mines, and the Swiss Federal Institute of Technology (ETH Zurich). By combining acoustic emission, ultrasonic monitoring, fiber-optic sensing, full-field digital image correlation, numerical modeling, and artificial intelligence, his laboratory investigates geomaterial behavior across spatial and temporal scales, from grain-scale deformation and distributed microcracking to damage localization, fracture coalescence, and macroscopic failure. This integrated framework seeks not merely to observe how geomaterials fail, but to uncover the governing physics of failure, understand how instability emerges from interacting processes, and ultimately develop physics-informed AI models for detecting precursors and forecasting failure in complex geological and engineered systems.
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