معرفی
Joonpyo Kim serves as Assistant Professor in the Department of Mathematics and Statistics at Sejong University since 2022, specializing in advanced statistical methodologies with applications across medical imaging and industrial engineering. His academic foundation includes doctoral training at Seoul National University where he developed expertise in non-standard statistical frameworks.
Education
- B.S. in Mathematics, Seoul National University (2016)
- Ph.D. in Statistics, Seoul National University (2022)
Dr. Kim's research centers on spatio-temporal data analysis using asymmetric norm frameworks and change point detection methodologies. His work bridges theoretical statistics with practical applications in ophthalmology and semiconductor manufacturing, particularly through functional data analysis and quantile-based techniques. The fingerprint of his research reveals strong connections to quantile theory (100%), functional data analysis (61%), and changepoint detection (61%), demonstrating interdisciplinary impact across medical diagnostics and industrial process control.
Recent publications (2024-2025) exhibit a clear trajectory toward solving high-dimensional statistical challenges in both biomedical and engineering contexts. His work on vitelliform macular dystrophy combines multimodal imaging with chromatic perimetry, while semiconductor manufacturing research develops novel model averaging for two-way functional data. The consistent application of quantile curves and extreme value theory across diverse domains highlights his methodological innovation.
Scientific Recognition
- Korean Statistical Society Best Ph.D Dissertation Award (2022)
- Sejong Research Fellowship (2022-2027) from Ministry of Science and ICT
- BK21 FOUR Groups Students Award from Seoul National University
Dr. Kim leads the Sejong Research Fellowship project (2022-2027), focusing on statistical methodology development with applications in medical diagnostics and industrial quality control. His mentorship contributed to the BK21 FOUR Groups Students Award, reflecting effective guidance in collaborative research environments. Current projects integrate multiscale extreme value analysis with functional data clustering techniques.
