Spencer Fargusson, a senior undergraduate in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois Urbana-Champaign, and an undergraduate research assistant in the Socio-Technical Risk Analysis (SoTeRiA) Research Laboratory led by Professor Zahra Mohaghegh, recently attended the prestigious Summer School on Implementation of Emerging Technologies in Halden, Norway.
Hosted by the Institute for Energy Technology (IFE) and organized by the Nuclear Energy Agency (NEA) through its Nuclear Education, Skills, and Technology (NEST) program, the Halden Summer School welcomed just 10 students globally, with Fargusson among them.
The program, part of the Halden HTO Project, aims to train early-career nuclear professionals and build global competence in the nuclear industry. This year’s workshop featured over 70 participants and presenters from across the world, including Norway, the United States, the UK, Canada, Germany, and more—offering a truly international perspective on implementing advanced technologies like Artificial Intelligence and Digital Twins in nuclear power plants.
As part of his work in the SoTeRiA lab, Spencer has contributed to an NRC-sponsored project focused on probabilistic risk assessment (PRA) of emerging technologies. His efforts have included developing methodologies to model the interaction between operator maintenance performance, Digital Twins, and physical systems in nuclear plants’ PRA.
“Being able to see how professionals around the world are bridging the gap between current practices and the kind of research I’m doing on human–digital twin interactions has been incredibly insightful,” Fargusson said. “This workshop offered a rare opportunity to collaborate across countries and generations, tackling complex challenges through global cooperation.”
After completing the NRC project, Spencer continued his work in the SoTeRiA research laboratory, collaborating with graduate student Hammad Khalid, Dr. Ha Bui, Dr. Seyed Reihani, and Prof. Zahra Mohaghegh on an NSF-funded project focused on analyzing socio-technical risk factors in AI-driven technologies used in nuclear power plants.