Congjian Wang
Senior Computational Nuclear Scientist
Idaho Falls, Idaho, United States
Summary
👤
Senior🎓
Top SchoolCongjian Wang is a Senior Computational Nuclear Scientist at Idaho National Laboratory with 11 years of experience building and leading advanced uncertainty quantification, probabilistic risk, and AI-enabled modeling tools for nuclear and energy systems. He leads development of the award-winning RAVEN framework and has driven projects spanning Bayesian calibration, experiment design automation, digital I&C risk assessment, and graph-based reliability analytics. His work blends reactor physics, applied mathematics, and high-performance computing with hands-on software engineering in Python and C++, integrating tools like MOOSE, SCALE, RELAP5-3D, and PARCS. He has pioneered applications of deep reinforcement learning, causal inference, and foundation-model techniques to optimize reactor operations, predictive maintenance, and integrated energy systems. A PhD in Nuclear Engineering and a track record of multidisciplinary collaborations and SBIR/ARPA-E projects underline his ability to translate cutting-edge research into deployable, risk-informed solutions. Colleagues rely on him not only for technical depth but also for pragmatic leadership in turning complex simulations into decision-grade insights.
11 years of coding experience
13 years of employment as a software developer
Ph. D, Nuclear Engineering, GPA 4.0, Ph. D, Nuclear Engineering, GPA 4.0 at North Carolina State UniversityBachelor, Engineering Physics, Bachelor, Engineering Physics at Tsinghua University
chinese, english
Github Skills (113)
raven10
risk-analysis10
parallel10
python10
stochastic10
deep-learning10
cython10
uncertainty10
amr10
optimization10
pde10
validation10
hpc-applications10
model-optimization10
neural-network10
Programming languages (3)
Github contributions (5)
idaholab/BayCal
Dec 2022 - Sep 2024
Contributions:1 release, 12 PRs, 42 pushes in 1 year 9 months
parametersvirtual-environmentriskravensimulation
idaholab/LOGOS
May 2019 - Oct 2022
Discrete optimization models (i.e., stochastic optimization, distributionally robust optimization and conditional value-at-risk optimization) that can be employed for capital budgeting optimization problems
Contributions:31 reviews, 331 commits, 26 PRs in 3 years 6 months
budgetingstochastic-optimizationoptimization-modelsproblemsoptimization
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