Jin Bae
Technical Professional at University Housing, University of Illinois
Knoxville, Tennessee, United States
Summary
👤
Senior🎓
Top SchoolJin Bae is a computational nuclear engineer with nine years of experience delivering reproducible, data-driven neutronics and fuel-cycle analysis at Oak Ridge National Laboratory for sponsors including DOE, NRC, and NNSA. He specializes in fission and fusion reactor design and optimization, applying high-fidelity Monte Carlo simulation, genetic algorithms, uncertainty quantification, and custom HDF5-enabled workflows to accelerate agile design iterations. Jin has led lattice and fuel-design optimizations for HFIR, HALEU PWR/BWR, and molten salt concepts, and developed GIS and depletion tools that connect technical analysis to siting and supply-chain implications. Equally comfortable in code and physics, he contributes to open computational toolchains (including metaheuristic algorithm work on EvoloPy) and integrates data-science practices into computational nuclear engineering. His background also includes systems-level thinking from military logistics and community leadership, which informs clear communication across multi-organization teams.
9 years of coding experience
2 years of employment as a software developer
University of Illinois Urbana-Champaign
English, Korean, Spanish
Github Skills (9)
algorithms10
evolutionary-algorithms10
metaheuristics10
python10
optimization10
numpy9
application-framework9
app-framework9
web-framework9
Programming languages (9)
Github contributions (5)
7ossam81/EvoloPy
Mar 2019 - Mar 2019
EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 commits, 1 PR, 1 issue in 1 day
Contributions summary:Jin primarily contributed to the implementation of various metaheuristic algorithms for global optimization within the EvoloPy toolbox. They modified the code for Particle Swarm Optimization (PSO), Bat Algorithm (BAT), Firefly Algorithm (FFA), Grey Wolf Optimizer (GWO), Cuckoo Search (CS), Moth-Flame Optimization (MFO), Salp Swarm Algorithm (SSA), and Whale Optimization Algorithm (WOA), adding boundary checks and adjustments. The user's work involved adapting and refining the core algorithm implementations, ensuring that the algorithms could handle a multivariate constrained environment.
optimization-methodsglobal-optimizationevolutionary-algorithmstoolboxoptimization
jbae11/d3ploy
Feb 2018 - Mar 2019
A collection of Cyclus manager archetypes for demand driven deployment
Contributions:79 PRs, 243 pushes, 34 branches in 1 year
archetypesdevopsdeploymentitildemand
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