Publications

Growth Prediction of Pacific Abalone Using Monotonicity-Constrained Stacked Gaussian Processes with Conformal Calibration

Won Park, Donghwan Kim, Seonghyeok Park, Seung-Won Seo, Ho-Jin Jung, Hyunju Ro, Han-Kyu Lim, and Seongil Jo

Diffusion-Transformer Hybrid Framework for Dataset Construction in Deep Learning-Based TEL Detection from Satellite Imagery

Soomin Kwon, Jeongin Lee, Seongil Jo, and Jaeoh Kim

Multi-Kernel Gaussian Process Stacking for Boiling Point Prediction with Calibrated Uncertainty

Seonghyeok Park, Hyoju Kim, Keunhog Jeong, Seongil Jo, and Jaeoh Kim

A Domain-Informed Composite Kernel Gaussian Process Classifier for Molecular Toxicity Prediction with Principled Uncertainty Quantification

Junhee Kim, Seongil Jo, Joonyeon Koo, Jaeoh Kim and Keunhong Jeong

Journal of Cheminformatics

Gaussian Process-Based Bayesian Optimization for One-Class Classification: A Comparative Empirical Study

Inyoung Beak, Junhee Kim, Yeongmin Lee, Jaeoh Kim and Seongil Jo

Computational Statistics

A Bayesian Deep Kernel Regression Approach to Nonstationary Sensor Data Analysis

Gyumin Choi and Seongil Jo*

Journal of the Korean Data & Information Science Society, 36(6), 957-972

Predicting flatfish growth in Aquaculture using Bayesian deep kernel machines

Junhee Kim, Seung-Won Seo, Ho-Jin Jung, Hyunseok Jang, Han-Kyu Lim, and Seongil Jo*

Applied Sciences, 12(17), 9487

Identification of functional dynamic brain states based on graph attention networks

Inyoung Baek, Jongyoung Namgung, Yeongjun Park, Seongil Jo*, and Bo-young Park*

Neuroimage, 311(1), 121185

A weighted Bayesian kernel machine regression approach for predicting the growth of indoor-cultured abalone

Seung-Won Seo, Gyumin Choi, Ho-Jin Jung, Mi-Jin Choi, Young-Dae Oh, Hyun-Seok Jang, Han-Kyu Lim*, and Seongil Jo*

Applied Sciences, 15(708), 1-16

A study on Bayesian beta regressions for modelling rates and proportions

Jeongin Lee, Jaeoh Kim, and Seongil Jo*

The Korean Journal of Applied Statistics, 37(3), 339-353