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Mr. Jongyun Byun

Ph.D. Student

He aspires to become an expert in hydroinformatics, specializing in handling and processing various types of data in the field of hydrology. His primary interest lies in developing image-based rainfall estimation algorithms that have the potential to replace traditional observation devices. Additionally, he is focused on creating fusion algorithms for hydrological and meteorological data, particularly in the integration of remote sensing data and numerical weather prediction models using machine learning techniques. His goal is to efficiently extract and utilize the features of these datasets. In the future, he aims to contribute to the digital-twin domain by merging and analyzing large-scale hydrological and meteorological datasets. Ultimately, he hopes to play a role as a technician who supports the resolution of broader societal challenges through advanced data analysis and integration.

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Education

Sep. 2024 – Present 

Doctor of Philosophy (Ph.D.) Course, Korea University

Department of Civil, Environmental and Architectural Engineering

  • Major: Water & Ecosystems (under supervision of Prof. Changhyun Jun)

Mar. 2023 – Aug. 2024 

(dropout & transfer) / Doctor of Philosophy (Ph.D.) Course, Chung-Ang University

Department of Civil Engineering

  • Major: Water Resources and Coastal Engineering (under supervision of Prof. Changhyun Jun)

Sep. 2021 – Feb. 2023 

Master of Engineering (M.E.), Chung-Ang University 

Department of Civil Engineering

  • Major: Water Resources and Coastal Engineering (under supervision of Prof. Changhyun Jun)

  • Title of M.E. Thesis: A Study on CCTV Image Data Pre-processing Techniques for Improving Accuracy in CNN-based Rainfall Intensity Estimation Model

Mar. 2016 – Aug. 2021 

Bachelor of Science (B.S.), Chung-Ang University 

School of Civil and Environmental Engineering, Urban Design and Studies, College of Engineering

  • Major: Civil Engineering (under supervision of Prof. Changhyun Jun)

  • Title of B.S. Thesis: Method for Precipitation Prediction based on Regression Analysis and Machine Learning

Get in Touch

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