Mohamed Bayoumy

Mohamed Bayoumy is an Assistant Professor at the University of Pittsburgh, specializing in optical fiber sensors, materials science, and their applications in extreme environments like nuclear reactors and energy systems. His work focuses on developing radiation-resistant sensors, machine learning-enhanced monitoring, and distributed fiber sensing technologies. He actively contributes to advancing sensor design for harsh conditions and energy sectors.

Research interests include distributed fiber optic sensing, radiation tolerance in materials, machine learning for sensor data analysis, and applications in nuclear engineering, additive manufacturing, and hydraulic fracturing. His projects often integrate optical fibers for real-time monitoring of temperature, strain, and environmental parameters.

Presentation topics include survey-based studies on remote education during the pandemic, project-based learning in optics, and sensor behavior prediction using neural networks. A patent on optical fiber-based sensing for electrical cables and radiation detection reflects his innovation in applied sensor technologies.

Key collaborations involve institutions like Oak Ridge National Laboratory (Daw, Carpenter), Penn State (Ohodnicki, Buric), and international partners. His work bridges fundamental materials science with applied engineering solutions for energy and infrastructure challenges.