Sungchul Hong
23.12.11. Smoothie King Center
I teach robots how to learn.
I am Sungchul Hong (홍성철), a postdoctoral researcher in the Department of Statistics at University of Seoul. I received Ph.D. (under the supervision of Prof. Jong-June Jeon) in Statistics at University of Seoul. My main research interest is risk-averse AI that can be applied to finance and robotics. In addition, I am also interested in time-series forecasting based on deep learning models.
Contact:
e-mail: shong@uos.ac.kr
Github: github.com/sungchulhong
Linkedin: linkedin/sungchulhong
Address: Mirae building 401-2, 163, Seoulsiripdae-ro Dondaemun-gu, Seoul, Republic of Korea.
Position
2023.03. ~ Present. Postdoctoral researcher, Department of Statistics, University of Seoul
2023.03. ~ 2024.02. Lecturer, Department of Urban Big Data Convergence, University of Seoul
2024.03. ~ 2025.02. Lecturer, Department of Statistics, University of Seoul
Education
2012.03 ~ 2018.02, B.S., Statistics, University of Seoul
2018.03 ~ 2020.02, M.S., Statistics, University of Seoul (Thesis: Grouped Portfolio Optimization with Pessimistic Risk Measure)
2020.03 ~ 2023.02, Ph.D., Statistics, University of Seoul (Dissertation: Uniform Pessimistic Risk and its Applications)
Research Interests
Portfolio optimization & quantitive investing strategies
Deep learning & its applications (finance, hydrology, climate change)
Statistical learning (quantile regression, density estimation)
In Progress (*: corresponding author, †: co-fisrt author)
Interpretable Spatiotemporal Forecasting. Sungchul Hong, Yunjin Choi, and Jong-June Jeon*. under revision.
Kernel Clustering Method. Beomjin Park, Changyi Park, Sungchul Hong, and Hosik Choi*. under revision.
Uniform Pessimistic Risk and its Optimal Portfolio. Sungchul Hong and Jong-June Jeon*. under review.
Deep Distributional Learning. Seunghwan An, Sungchul Hong, and Jong-June Jeon*. under revision.
Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering. Sungchul Hong, Seunghwan An, and Jong-June Jeon*. under review.
U-Net for Sea Ice Concentration Forecasting. Jaesung Park†, Sungchul Hong†, Yoonseo Cho, and Jong-June Jeon*. submitted.
Adaptive Adversarial Augmentation for Molecular Property Prediction. Soyoung Cho, Sungchul Hong, and Jong-June Jeon*. submitted.
Forecasting Multivariate Time-series with Deep Generative Model. Seunghwan An†, Sungchul Hong†, and Jong-June Jeon*. preprint.
Multi-Factor Stock Selection with Transformer.
Publications (*: corresponding author, †: equal contribution)
Finding an NARE whose minimal nonnegative solution represents first passage quantities in the two-dimensional Brownian motion. Sungchul Hong and Soohan Ahn*. Journal of the Korean Statistical Society (JKSS), 2024.
Clustering for Regional Time Trend in the Nonstationary Extreme Distribution. Sungchul Hong, Jong-June Jeon*, and Yongdai Kim. Water, 2022.
Application of GTH-like algorithm to Markov modulated Brownian motion with jumps. Sungchul Hong and Soohan Ahn*. Communications for Statistical Applications and Methods, 2021.
Selected Work Experience
Clustering the volatility of cryptocurrency based on market price, Financial Supervisory Service (금융감독원), 2022.
Robo-advisor development, GwanakLab (관악연구소), 2023 ~.
Books (*: translation)
실무로 통하는 인과추론 with Python* (Causal Inference in Python, Korean ver.). 마테우스 파쿠레 저. 신진수, 가짜연구소 인과추론팀 역. 박지용 감수
Lecturer@UOS
2023-Spring
Introduction to Statistics (undergraduate)
Python and Deep Learning Programming (graduate)
2023-Fall
Data Visualization (graduate)
2024-Spring
Python Programming (graduate)
Awards
한국인공지능학회 & NAVER 추계 공동학술대회 우수논문상, 2022. (pdf)
한국통계학회 추계학술논문발표회 대학원생 우수논문발표상, 2019.