CV

Undergraduate Researcher

Vision and Learning Lab (UVLL), UNIST

Advised by Prof. Seungryul Baek

• Focused on 3D vision, human mesh recovery (HMR), and Vision-Language-Action (VLA)
• Studied SMPL-based human mesh recovery and motion reconstruction
• Surveyed and presented recent research trends in egocentric vision and VLA at lab seminars

Undergraduate Researcher

Multiscale Lab, Jeonbuk National University

Advised by Prof. Guebin Choi

• Focused on data-adaptive approaches in multiscale analysis
• Studied deep learning applications in NLP and computer vision
• Prepared a research poster and participated in an academic conference

Teaching Assistant, Statistical Thinking and Society

Jeonbuk National University

• Prepared and reviewed instructional materials

Participant

LG Aimers (6th)

• Completed coursework on Artificial Intelligence and Linear Algebra
• Participated in a data competition using infertility-related medical data

Member

JBNU Big Data Club (JBIG)

• Participated weekly in Dacon and Kaggle competitions, followed by code presentations during club seminars

M.S.

Korea Advanced Institute of Science and Technology (KAIST)

Graduate School of Data Science (GSDS), KIRC Lab

• Incoming M.S. student (starting March 2027)
• Research interests: fine-tuning Large Language Models (LLMs) specialized for the medical domain

B.S.

Jeonbuk National University

Statistics, College of Natural Sciences

• Total GPA: 4.40 / 4.5, Major GPA: 4.47 / 4.5

Medical Image Analysis

• Collaborating with a university hospital on deep learning-based medical image analysis
• Developing AI models for clinical data analysis and diagnostic support
• Working on improving the accuracy and efficiency of medical imaging workflows

Deep Neural Networks for Protein Function Prediction

• Developed and benchmarked deep learning approaches for enzyme function prediction using the ESM-2 protein language model
• Implemented ESM-2 + Random Forest and ESM-2 + Multi-Layer Perceptron architectures to predict Enzyme Commission (EC) numbers
• Achieved the best performance with the MLP approach (Micro F1: 0.89, Macro F1: 0.77), improving over similarity-based methods
• Applied hyperparameter optimization and class imbalance handling to improve performance on rare enzyme classes

Deep Learning-based Neural Granger Causality for Solar Radiation Data

• Addressed the limitations of traditional VAR models in capturing nonlinear dependencies in time series data
• Implemented component-wise MLP (cMLP) and LSTM (cLSTM) models, assigning an independent network to each variable
• Analyzed Granger causal relationships between solar radiation in Jeonju and key meteorological variables
• Identified causality structures consistent with established relationships in solar meteorology

Academic Excellence Scholarship, Sancheong-gun Local Scholarship

Sancheong-gun

Received in 2021.05, 2024.05, 2024.11, 2025.05, 2025.11, and 2026.05

Challenge Award, J-Point Scholarship Program

JBNU SW-Center

First Prize, 2024 Big Data Camp - Prompt Engineering Competition

COSS

Yellow Belt, Keun-Saram (Great Person) Project

Jeonbuk National University

Grand Prize, 2024 Jeonbuk National University AI Competition

Jeonbuk National University