Curriculum Vitae

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Education

March 2026 – Present

Master of Science in School of Computing

Korea Advanced Institute of Science and Technology (KAIST)

Current
  • Collaborative Social Technologies Lab (Advisor: Prof. Joseph Seering)

March 2020 – February 2026

Bachelor of Science in Electrical Engineering

Korea Advanced Institute of Science and Technology (KAIST)

Magna Cum Laude
  • GPA: 3.88/4.3 (95.80/100)

Experience

September 2025 – Present

Undergraduate Research Intern

KAIST Collaborative Social Technologies Lab (Advisor: Prof. Joseph Seering)

CurrentResearch

Research on collaborative group interaction analysis and dataset construction

  • Contributing to an audio-to-text dataset construction pipeline for group collaboration recordings
  • Enhancing speaker diarization system through parallelization and speaker embedding techniques

August 2024 – February 2025

Intern, S/W Development Team

Samsung Electronics, Device Solution Division

Industry

Development of advanced LLM service enhancement project

  • Developed PIR-based Query Decomposition Unit: 28% search quality improvement, 43% zero-hit query reduction
  • Implemented Query-Document Relevance Check with DeepSeek-R1-Distill-Qwen-32B: 98.1% score stability
  • Built RAG evaluation system applied to 3,000+ real-world evaluations
  • Researched Korean-optimized LLM prompt engineering techniques

June 2024 – August 2024

Independent Research

KAIST Integrated Vision Language Lab

Research

Research on LLM and RAG system optimization

  • Comparative research on standard vs reasoning-enhanced LLMs
  • Investigated optimal retrieval methodologies for RAG systems
  • Fine-tuning experiments for Korean-specific LLM performance

January 2024 – February 2024

Independent Research

KAIST Neuro-Instrumentation & Computational Analysis Lab

Research

Research on deep neural networks for computer vision

  • In-depth study based on Stanford CS231n curriculum
  • CNN architecture design and optimization
  • Image segmentation, object detection, and image generation implementation
  • Deep learning training pipelines with PyTorch

Publications

Papers in preparation — to be updated.

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