Hyun Han Kwon is interested in research on artificial intelligence and big-data analysis, with a strong focus on machine-learning techniques such as support vector machines (SVM), ensemble modelling, neural networks, and Bayesian network modelling. He is particularly skilled in applying Bayesian modelling to large-scale climate analysis, yielding significant results from extensive datasets. Hyun Han's research portfolio also includes work in statistical and stochastic hydrology, climate change modelling, downscaling modelling, time-series modelling, risk analysis, and both parametric and non-parametric regional frequency analysis. He has conducted hydrologic modelling, including rainfall-runoff and river modelling, and has experience with geographic information systems (GIS) and satellite data manipulation. He is proficient in numerous hydrology and hydraulics software programs, including HEC-HMS, HEC-RAS, SAC-SMA (NWS-PC), SMADA, and RRL. His expertise with these tools enables him to model, analyse, and manage water resources and hydrologic systems effectively, ensuring precise and reliable outcomes across various projects.