Statistics & Learning for Applied Biomedicine

SLAB

The foundation, the floor, the framework.

전북대학교 통계학과 · Department of Statistics · Jeonbuk National University

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03

세 가지 연구 방향
의료영상과 임상 AI, 통계 방법론 개발, 인구집단 역학.

Statistics as structure

Three lines of research: biomedical imaging and clinical AI, statistical methodology development, and population health epidemiology.

01 Biomedical Imaging & Clinical AI — CT / MRI / fMRI, Neuroimaging, GLMM · GEE, DL Reproducibility, Survival Analysis

02 Statistical Methodology Development — Fiducial Inference, Dynamic Networks, Brain Connectivity, Social Networks, R / Python Packages

03 Population Health & Epidemiology — KNHANES, Complex Survey Design, Nutritional Epidemiology, K-Food, GLM

01 — Biomedical Imaging & Clinical AI

Medical imaging
meets statistical rigor.

We develop and evaluate statistical and AI-based methods for medical image analysis — spanning CT, MRI, fMRI, and DTI — with emphasis on rigorous validation and reproducibility of clinical AI systems.

Research areas include neuroimaging processing, pulmonary CT modeling, and fairness & reproducibility evaluation of deep learning diagnostics.

CT / MRI / fMRI Neuroimaging GLMM · GEE DL Reproducibility Survival Analysis

Students will learn

→Medical image data preprocessing & analysis
→GLMM, GEE, Cox PH, competing risks
→DL model evaluation & reproducibility
→Neuroimaging analysis pipelines

02 — Statistical Methodology

Building methods
from the ground up.

We develop novel statistical methods for complex, real-world data and validate them through rigorous simulation and application. Current focus areas include uncertainty quantification and dynamic network inference.

Methods are developed for direct application: generalized fiducial inference for complex models, and dynamic network clustering for time-varying social, biological, and brain networks.

Fiducial Inference Dynamic Networks Brain Connectivity Social Networks R / Python Packages

Students will learn

→Statistical method development end-to-end
→Network analysis & graph theory
→R/Python package development
→Simulation study design

03 — Population Health & Epidemiology

Data that connects
diet to disease.

Using large-scale population data including KNHANES, we investigate associations between dietary patterns, lifestyle factors, and chronic disease outcomes — with a focus on Korean food culture and public health.

We specialize in complex survey design analysis and appropriate statistical modeling for nationally representative epidemiological data.

KNHANES Complex Survey Design Nutritional Epidemiology K-Food GLM

Students will learn

→Complex survey data analysis
→Large-scale public data (KNHANES, 건보)
→Epidemiological study design
→Logistic / Poisson regression, GLM

People

Our Team

PI

Members

Alumni

“Seungyong Hwang"

황승용 · Assistant Professor

Ph.D. in Biostatistics from UC Davis under the supervision of Prof. Thomas C.M. Lee and Prof. Jie Peng. Completed postdoctoral training in the Department of Genetics at Stanford University. Previously Senior Biostatistician at GRAIL, Inc.

Now Assistant Professor in the Department of Statistics and Institute of Applied Statistics at Jeonbuk National University. Research spans statistical methodology development, medical imaging analysis, biomedical AI evaluation, and nutritional epidemiology.

Email Google Scholar GitHub CV

Namyoon Kim

Undergraduate student

Research interest placeholder

N/A

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Publications

Research Output

Published

Under Review

In Preparation

SCIE Co-Author

Gochujang attenuates colorectal cancer by promoting colonic SCFA utilization and GPCR expression despite limited recovery of microbial diversity

Baek J, Kim J, Jeong D, Hwang S, Donohoe D, Han A

SCIE Co-Author

CNNeoPP: a large language model-enhanced deep learning pipeline for personalized neoantigen prediction and liquid biopsy applications

Cai Y, Chen R, Song M, Wang L, Huo Z, Yang D, Zhang S, Gao S, Hwang S, Bai L, Lv Y, Cui Y, Zhang X

SCIE Co-Author

Contextualized biomedical language processing enhances ICU survival prediction

Chen R, Cai Y, Zhang S, Huo Z, Song M, Li W, Yang D, Hwang S, Bai L, Han F, Zhang X

SCIE First Author

Estimating fiber orientation distribution with application to study brain lateralization using HCP D-MRI data

Hwang S, Lee T, Paul D, Peng J

SCIE Co-First Author

Diagnostic Accuracy of Magnetic Resonance Imaging Features and Tumor-to-Nipple Distance for Nipple-Areolar Complex Involvement: A Systematic Review and Meta-Analysis

Byon J, Hwang S, Choi E, Choi H

SCIE First Author

Generalized Fiducial Inference for Threshold Estimation in Dose-Response and Regression Settings

Hwang S, Lai C, Lee T

SCIE Co-Author

Simultaneous detection of multiple change points and community structures in time series of networks

Cheung RCY, Aue A, Hwang S, Lee T

First Author Under Review

Estimating Spatially-Smoothed Fiber Orientation Distribution from Diffusion-MRI Experiments

Hwang S et al.

First Author Under Review

Hierarchical Clustering in time-evolving dynamic networks