ASNT… Creating a Safer World! ™

2020 Faculty Grant Recipients

Parisa Shokouhi

Dr. Shokouhi is an Associate Professor of Engineering Science and Mechanics at Penn State. Prior to coming to Penn State, Dr. Shokouhi was a research group leader in Nondestructive Testing Department of BAM - Federal Institute for Materials Research and Testing, Berlin, Germany - and a visiting Professor at Los Alamos National Laboratory (LANL). Her main research experience and expertise include: Stress wave propagation in fractured media, Nondestructive evaluation (linear and nonlinear ultrasonic testing), Structural health monitoring (acoustic emission), Machine learning, and Seismic metamaterials. Her current research projects are supported by Department of Energy (DOE), National Science Foundation (NSF), National Institute for Standards and Technology (NIST), US Department of Transportation (USDOT) and Pennsylvania Department of Transportation (PennDOT), among others. She is a recipient of Alexander von Humboldt and 2019 American Society of Nondestructive Testing (ASNT) Fellowship Awards.

Xuan Zhu

Peter Zhu is currently an assistant professor at department of civil and environmental engineering at the University of Utah. He obtained his B.S. from Beijing University of Aeronautics and Astronautics, M.S. from University of Pittsburgh, and Ph.D. from the University of California, San Diego. He has been conducting research that spans the breadth of experimental, theoretical, and numerical approaches in the fields of NDT and experimental mechanics, along with their applications in transportation and energy infrastructure. He received many awards for academic excellence, such as Charles Lee Powell Fellowship, Dissertation Fellowship, and NSF Scholarship. He also serves as reviewer for several top international journals in his research field. He delivered multiple invited presentations in leading research universities in the world. Since 2018, he has been developing and teaching a graduate course at the University of Utah on ‘Infrastructure sensing and health monitoring’ with a strong emphasis on NDT technologies for civil and mechanical systems. And he plans to introduce a brand new module on ‘machine learning in NDT’ to bring in new ideas and convince more students into the career path of NDT.