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

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)

Portfolio Optimization & Quantitive Investing Strategies

Deep Learning & its Applications (finance, hydrology, climate change)

Statistical Learning (quantile regression, density estimation)

Sparse Kernel K-means Clustering. Beomjin Park, Changyi Park, Sungchul Hong, and Hosik Choi*. Journal of Applied Statistics, 2024.

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, 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. 

Uniform Pessimistic Risk and its Optimal Portfolio. Sungchul Hong and Jong-June Jeon*. preprint . submitted.

Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering. Sungchul Hong, Seunghwan An, and Jong-June Jeon*. preprint . 

Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations. Jaesung Park†, Sungchul Hong†, Yoonseo Cho, and Jong-June Jeon*.  preprint . submitted.

Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance. Seunghwan An, Sungchul Hong, and Jong-June Jeon*. preprint.

Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis. Seunghwan Ah, Gyeongdong Woo, Jaesung Lim, Changhyun Kim, Sungchul Hong, and Jong-June Jeon. preprint.

Interpretable Spatiotemporal Forecasting. Sungchul Hong, Yunjin Choi, and Jong-June Jeon*. under revision.

Adaptive Adversarial Augmentation for Molecular Property Prediction. Soyoung Cho, Sungchul Hong, and Jong-June Jeon*. under review.

Cryptocurrency Price Forecasting using Variational AutoEncoder with Versatile Quantile Modeling. Sungchul Hong†, Seunghwan An†, and Jong-June Jeon*. submitted.

Efficient Nonlinear Quantile Factor Models for Asset Pricing via Variational AutoEncoder. Sungchul Hong, Seunghwan An, Kijeong Park, and Jong-June Jeon*.

Time Series Anomaly Detection with Variational AutoEncoder. Sungchul Hong*.

Deep Learning For Stock Price Forecasting. Kijeong Park, Sungchul Hong,  and Jong-June Jeon*.

Clustering the volatility of cryptocurrency based on market price, Financial Supervisory Service (금융감독원), 2022.

Robo-advisor development, GwanakLab (관악연구소), 2023 ~.

ML-based fraud detection model with Artificial Intelligence, KB Kookmin Bank (KB국민은행), 2024 ~.

2023-Spring@UOS

1. Introduction to Statistics (undergraduate)

2. Python and Deep Learning Programming (graduate)

2023-Fall@UOS

1. Data Visualization (graduate)

2024-Spring@UOS

1. Python Programming (graduate)

한국인공지능학회 & NAVER 추계 공동학술대회 우수논문상, 2022. (pdf)

한국통계학회 추계학술논문발표회 대학원생 우수논문발표상, 2019.