CV
Basics
Name | Minseon Kim |
Label | PhD Student |
mkim54@ncsu.edu | |
Phone | +1-984-382-3723 (US) / +82-10-7318-2205 (KR) |
Url | https://www.linkedin.com/in/kim-minseon |
Summary | A PhD student in Computer Science at NC State University |
Work
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2025.03 - 2025.05 AI Engineer
AIBIZ Global Co., Ltd.
- Improved anomaly detection by incorporating temporal dependencies into a GNN for semiconductor time-series data.
- Designed and developed a multi-level graph representation learning pipeline to capture both local and global sensor interactions.
- Implemented automated Root Cause Analysis using LangChain with DAPT.
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2022.07 - 2022.08 Back End Developer
PFCT - PFC Technolgies
- Developed real-time stock data pipeline using Microservice Architecture.
- Designed and implemented RESTful APIs using Django and Django REST Framework.
Education
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2025.08 - Present Raleigh, NC, USA
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2023.03 - 2024.08 Seoul, Korea
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2017.03 - 2023.02 Seoul, Korea
Projects
- 2025.03 - 2025.04
Anomaly Detection for Outgoing Quality Control of Camera Modules
LG Innotek, Inc.
- Extracted patterns of personal access behavior and labeled log data through statistical analysis.
- Developed an unsupervised LSTM model, decomposing log data into distinct keys and parameters.
- 2024.01 - 2024.07
ML-based Analysis of Cyclic Plasticity Behaviour of a Pipe Bend
LG Electronics, Inc.
- Enhanced the prediction accuracy of elastic shakedown boundaries through machine learning techniques.
- 2023.02 - 2023.12
A Self-Training based Machine Learning Platform for SOAR
AhnLab, Inc.
- Incrementally updated the tokenizer for newly generated web attack payloads.
- Implemented continual learning method for global model with local data to improve the performance.
- Developed REST APIs for data balancing, model training, and inference.
- 2022.05 - 2022.10
Realtime Prediction of Mechanical Equipment Failure based on Federated Learning
Kozen, Corp.
- Extracted patterns of personal access behavior and labeled log data through statistical analysis.
- Developed an unsupervised LSTM model, decomposing log data into distinct keys and parameters.
- 2020.12 - 2021.09
Anomaly Detection through Analysis of Large-Scale Personal Access Logs
Samo Convergence & Security, Corp.
- Extracted patterns of personal access behavior and labeled log data through statistical analysis.
- Developed an unsupervised LSTM model, decomposing log data into distinct keys and parameters.