SeungHun Lee (이승훈)
AI Engineer, 3billion
Ph.D. Candidate (Expected Graduation: July 2026), Network Data Science Lab
Dept. of Computer Software Engineering, Sangmyung University
Email: sh.lee@3billion.io / mr.leesh90@gmail.com

Curriculum Vitae

Professional Experience

• AI Engineer, 3billion, Seoul, South Korea (2026.3 ~ Present)
  • Developing foundation models for DNA, RNA, and protein sequences.
  • Applying deep learning architectures for large-scale biological sequence modeling.
  • Building scalable training pipelines for large-scale genomic datasets.

Education

• Ph.D., Dept. of Computer Software Engineering, Sangmyung University (2018.3 ~ 2026.7, Expected)
• M.S., Dept. of Computer Software Engineering, Sangmyung University (2015.3 ~ 2017.2)
• B.S., Dept. of Computer Software Engineering, Sangmyung University (2009.3 ~ 2015.2)

Research Interests

My research focuses on AI-driven modeling of biological sequences, particularly in genomics and molecular biology. I develop foundation models to understand genomic function and predict the impact of genetic variants. I am particularly interested in large-scale sequence modeling and representation learning for DNA/RNA/protein data, along with explainable AI (XAI) techniques to improve model interpretability in high-stakes domains.

Previous research: Network Traffic Classification, Crowdfunding Scam Detection, Multi-modal Data Analysis, Graph Neural Networks, NLP, and LLM applications.

Publications

Conferences

• Traffic Classification using Deep Learning: High Accuracy is Not Enough
  Kanghee Lee, Seunghun Lee, and Hyun-chul Kim
  ACM SIGCOMM, New York City, Aug. 2020.(Poster)

• Content-based Success Prediction of Crowdfunding Campaigns: A Deep Learning Approach
  Seunghun Lee, Kanghee Lee, and Hyun-chul Kim
  ACM CSCW, New York City’s Hudson River (Jersey City), Nov. 2018.(Poster)

• The Language of Deceivers: Linguistic Features of Crowdfunding Scams
  Wafa Shafqat, Seunghun Lee, Sehrish Malik, and Hyun-chul Kim
  International World Wide Web conference (WWW), Montreal, Canada, Apr. 2016.(Poster)

• SNS Map : Location-based SNS data mapping system
  Seunghun Lee, Daeyoung Oh, Minhyuk Kang, and Hyun-chul kim
  한국컴퓨터종합학술대회(In Proceedings of the Korea Computer Congress(KCC)), Jeju, Korea, June. 2015.

Journal

• Explainable Prediction of Crowdfunding Success Using Hierarchical Attention Network
  Seunghun Lee, Muneeb A. Khan, and Hyun-chul Kim
  Journal of MDPI Electronics, Volume 15, Number 3, Jan. 2026.

• Fraud Detection on Crowdfunding Platforms using Multiple Feature Selection Methods
  Seunghun Lee, Heemin Park, and Hyun-chul Kim
  IEEE Access, Mar. 2025.

• Backers Beware: Characteristics and Detection of Fraudulent Crowdfunding Campaigns
  Seunghun Lee, Wafa Shafqat, and Hyun-chul Kim
  Journal of MDPI Sensors, Volume 22, Number 19, pp. 7677, Oct. 2022.

• Crowdfunding Scams : The Profiles and Language of Deceivers
  Seunghun Lee, and Hyun-chul Kim
  Journal of The Korea Society of Computer and Information, Volume 23, Number 3, pp. 55-62, Mar. 2018.

• Predicting Success of Crowdfunding Campaigns using Multimedia and Linguistic Features
  Kang-hee Lee, Seunghun Lee, and Hyun-chul Kim
  Journal of Korea Multimedia Society, Volume 21, Number 2, pp. 281-288, Feb. 2018.

• Smart SNS Map: Location-based Social Network Service Data Mapping and Visualization System
  Jangho Yoon, Seunghun Lee, and Hyun-chul Kim
  Journal of Korea Multimedia Society, Volume 19, Number 2, pp. 428-435, Feb. 2016.
  
  

Projects

• Explainability in Graph Neural Networks for Internet Traffic Classification
  (sponsored by the National Research Foundation, Korea) 2023.06 ~ 2025.02
• Research on Virtualization-based 5G Networks and Cyber Threats
  (sponsored by National Security Research Institute, Korea) 2023.04~2023.10
• Deep Learning based Internet Traffic Classification: Myths, Realities, and their Explainabilities
  (sponsored by the National Research Foundation, Korea) 2022.06 ~ 2023.05
• Research on Building a Virtualization-Based 5G Cyber Training Environment
  (sponsored by National Security Research Institute, Korea) 2022.04 ~ 2022.10
• Multi-modal data-driven Explainable AI Systems and the Future of Digital Finance
  (sponsored by the National Research Foundataion, Korea) 2019.09 ~ 2022.02
• Towards Explainable AI in Next-Generation Intrusion Detection Systems
  (sponsored by National Security Research Institute, Korea) 2019.04~2019.10
• Statistics-based Network Behavior Modeling
  (sponsored by National Security Research Institute, Korea) 2018.05 ~ 2018.10
• Traffic Measurement in Anonymity Networks
  (sponsored by National Security Research Institute, Korea) 2017.04 ~ 2017.10
• Characterization and Automatic Labeling of Malicious Traffic in Control System Networks
  (sponsored by National Security Research Institute, Korea) 2017.04 ~ 2017.10
• Network Traffic Classification for Intrusion Detection
  (sponsored by National Security Research Institute, Korea) 2015.06 ~ 2015.12