About

🌠 Introduction

I am an AI Research Trainee at Aiffel Research 14th, Modulabs. My work focuses on Artificial Intelligence, Natural Language Processing (NLP), and Large Language Models (LLMs).

🔬 Research Interests

• Multilingual & Low-Resource NLP (representation learning, MT)
• Efficient & Lightweight Language Models (distillation, PEFT)
• Reliable & Responsible Generative Systems (fact-grounded dialogue)
• Vision-Language & Multimodal Reasoning for socially beneficial AI
  
  

📑 Research Overview

My research interests lie in Artificial Intelligence (AI), Natural Language Processing (NLP), and Large Language Models (LLMs), with a particular focus on efficient multilingual representation learning for low-resource languages, including machine translation, as well as lightweight pretraining and reliable dialogue systems.

I have worked on several projects and competitions, including the development of data-driven models for ESG credit scoring, chatbot dialogue systems with emotion analysis, and multilingual text analysis.

Through these experiences, I gained hands-on expertise in machine learning techniques such as XGBoost, CatBoost, and Transformer-based architectures, as well as practical knowledge in data preprocessing, clustering, and evaluation.

Looking ahead, my goal is to advance research in efficient and trustworthy multilingual NLP systems, and to build AI models that not only achieve high performance but also contribute to solving real-world challenges with reliability and social impact.

💼 Experience

AI Research Trainee | Modulabs — Remote (Seoul, Korea)

Jul 2025 – Jan 2026

• Applied Machine Learning and Deep Learning techniques to real-world datasets in NLP and Computer Vision
• Built and fine-tuned text classification and sentiment/emotion analysis models using PyTorch and Hugging Face Transformers
• Conducted experiments on CNNs and transfer learning for cross-domain Computer Vision tasks
• Performed data preprocessing and visualization (Python, Pandas, Matplotlib, Seaborn) to support model training and evaluation
  
  

Undergraduate Student Researcher | JBNU Cognitive Computing Lab (Jeonju, Korea)

Jul 2022 – Feb 2025

• Contributed to an industry–academia project on Neural Radiance Fields (NeRF) by implementing model components for 3D representation learning
• Supported baseline experiments and evaluation pipelines for follow-up research
• Attended deep learning study and paper review sessions to strengthen theoretical and practical foundations in NLP and Machine Translation
  
  

English Writing Center Tutor (TA) | JBNU English Department (Jeonju, Korea)

May 2024 – Dec 2024

• Delivered one-on-one tutoring sessions, providing feedback on structure, organization, and clarity
• Supported students in academic writing, creative idea development, and grammar refinement
• Helped undergraduate learners build confidence in English composition and critical thinking
  
  

🎓 Education

Jeonbuk National University | Jeonju, Korea

Mar 2020 – Aug 2025

Bachelor of Arts. in English Language and Literature

Bachelor of Science and Engineering. in Computer Science and Engineering (Double Major)

• Built a strong interdisciplinary foundation by combining language, literature, and computational methods
• Awarded SW Convergence J-Point Excellence Scholarship (Spring 2025) and Top Excellence Scholarship (Fall 2024) for outstanding achievement in Computer Science and Engineering field
  
  

🎯 Long-term Goals

My long-term goal is to contribute to the advancement of natural language processing and large language models in ways that promote linguistic equity and global accessibility.

I aim to push research in multilingual NLP, machine translation, and efficient model development, while building AI systems that meaningfully reduce language barriers and serve real communities.

Ultimately, I hope to work at the intersection of academia and industry to create technologies that are both technically innovative and socially impactful.

• Advance multilingual NLP research to better represent under-resourced languages
• Improve model efficiency and scalability, enabling broader access and real-world deployment
• Strengthen reliability and factual grounding in generative and dialogue-based systems
• Bridge research and application by developing systems that are usable, impactful, and globally accessible
  
  

🖥️ Tech Stack

• Programming Languages: Python, Java, JavaScript, C, Dart, C++, C#, HTML, CSS
• Machine Learning & Data Science: NumPy, Pandas, Scikit-learn, PyTorch, Seaborn, Tensorflow, Keras
• Natural Language Processing: KoBERT, KoBART, Hugging Face Transformers
• Frameworks & Tools: Jupyter Notebook, Google Colab, Git, React, Flutter, Next.js, Node.js
  
  

📄 Curriculum Vitae

You can download my latest CV here:

📷 Gallery