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INL National University Consortium
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North Carolina State postdoc fulfills research dreams at INL
Dr. Mohammad Abdo, a graduate of North Carolina State University (NCSU), has always had a passion for knowledge and learning. As an instructor, his research often was a lower priority to helping his students. That's why after many years of putting research second to teaching, Abdo decided to finally fulfill his dream of pursuing a postdoctoral researcher position at Idaho National Laboratory (INL).
Since beginning his two-year appointment in June as a member of the Risk Analysis and Virtual Environment (RAVEN) development team, Abdo has utilized his extensive background in data mining, artificial intelligence and machine learning.
As part of the RAVEN team, Abdo's research primarily focuses on using, adding and developing new algorithms to facilitate the creation of models and simulations that are less expensive, yet more efficient in modeling and testing nuclear energy applications. In the past, it was common for simulations to be costly and run for several days or weeks before seeing any sort of statistical result, and even then, researchers were not guaranteed the outcome they were anticipating. To help combat this, Abdo plans to make the simulations less expensive without sacrificing efficiency.
"My current research is to make use of the mines of data available in the national labs, universities, and industrial entities to inform experimentalists of the most influential measurables and help them build experiments that save both time and money," said Abdo.
A native of Alexandria, Egypt, Abdo grew up with a constant thirst for knowledge, always wanting to learn more and help others in his endless quest for enlightenment. He found himself placing top of his class in his early years, eventually earning a bachelor's degree in mechanical engineering at Alexandria University in 2000. Years later, he earned his doctorate in nuclear engineering from NCSU, ranked third among the nuclear graduate schools nationwide, in 2016.
After obtaining his Ph.D., Abdo served as a postdoctoral researcher at NCSU and Kansas State University (KSU). Following a year of instructing at the latter, he heard about a potential research opportunity with INL that he simply couldn't pass up.
"INL was always on my roadmap of places I dreamed to conduct research for," he said. "I had people tell me, 'you'd better apply for a tenure-track.' And I'd say, 'not before I gain this experience.' I feel like it's where I should be."
One of Abdo's most prominent projects in collaboration with his mentor, Dr. Aaron Epiney, is to help design the Transient Reactor Test (TREAT) Facility Water Environment Recirculating Loop. Abdo's modeling and simulation research will allow the TREAT team to better understand how sensitive the responses of interest are to the input parameters and initial conditions and will help identify all possible uncertainties. The goal is to create an indistinguishable parallel between the experimental data and the actual plant through the team's utilization of the representativity theory, which aims to accurately represent a simulated accident under controlled conditions using TREAT.
Abdo's work will eventually improve the fidelity and reliability of many nuclear simulations. His research with RAVEN will allow facilities like TREAT to conduct more experiments by generating results at a more cost-effective rate. How far away they are from sustainability is anyone's guess, said Abdo. However, he's confident these methodologies will prove to be indispensable.
Now that he's finally fulfilling his dream of pursuing research in his field of expertise, Abdo is beginning to think about the next step of his journey. Like many researchers, he hopes his work will eventually allow him to help find a cure for cancer using pattern recognition and machine learning techniques in genomics.
"Several members of my family have had it," he said, "so if I could participate in making cancer less common or cure it entirely, that would be a dream come true."
Date Published: 2019-09-16T06:00:00Z