Hoyoon Byun

Ph.D. Student, Department of Statistics and Data Science, Yonsei University
📧 hoyun.byun@yonsei.ac.kr
🌐 MLAI Lab @ Yonsei Univ.

About Me

I am a Ph.D. student in the Department of Statistics and Data Science at Yonsei University, advised by Prof. Kyungwoo Song. My research interests include causal representation learning, domain generalization, and large language models.

Research Interests

• Causal Representation Learning
• Domain Generalization
• Large Language Models

Education

• Yonsei University, Korea – Ph.D. in Statistics and Data Science (2024–Present)
  • Advisor: Kyungwoo Song
• University of Seoul, Korea – M.S. in Artificial Intelligence (2022–2024)
  • Advisor: Jiyoung Jung
  • Co-advisor: Kyungwoo Song (Yonsei University)
• Pukyong National University, Korea – B.S. in Electrical and Semiconductor Engineering (2014–2021)

Publications

Conference

1. Kangjun Noh, Baekryun Seong, Hoyoon Byun, Youngjun Choi, Sungjin Song, and Kyungwoo Song,
   “LBC: Language-Based-Classifier for Out-Of-Variable Generalization”, NAACL 2025.
2. Hakyung Lee, Keon-Hee Park, Hoyoon Byun, et al.,
   “CED: Comparing Embedding Differences for Detecting Out-of-Distribution and Hallucinated Text”, EMNLP 2024 Findings.
3. Hee-young Lee*, Hoyoon Byun*, Changdae Oh, et al.,
   “Perturb-and-Compare Approach for Detecting Out-of-Distribution Samples in Constrained Access Environments”, ECAI 2024.
4. Changdae Oh, Junhyuk So, Hoyoon Byun, et al.,
   “Geodesic Multi-Modal Mixup for Robust Fine-tuning”, NeurIPS 2023.

Journal

1. Hoyoon Byun, Sungjun Lim, and Kyungwoo Song,
   “GFML: Gravity Function for Metric Learning”, Engineering Applications of Artificial Intelligence (EAAI).
2. Jeyoon Yeom, Hakyung Lee, Hoyoon Byun, et al.,
   “TC-Llama 2: Korean Large Language Model for Technology-Commercialization Data”, Journal of Big Data (SCIE).

(*Equal Contribution)

Workshops

• “Exploring the Potential of Foundation Models as Reliable AI Contact Centers”, IJCAI 2024 Workshop (Oral Presentation)
• “H-Consistency Bounded Critic for Enhancing Retrieval-Augmented Generation”, IJCAI 2024 Workshop
• “LBC: Language-Based-Classifier for Out-Of-Variable Generalization”, IJCAI 2024 Workshop (Oral Presentation)
• “Flat Posterior For Bayesian Model Averaging”, IJCAI 2024 DSO Workshop

Teaching Experience

• Linear Algebra with Applications, University of Seoul (Sep. 2023 – Dec. 2023)
  Teaching Assistant

Research Projects

• AI-Based Personalized Blood Pressure Estimation (Apr. 2024 – Dec. 2024)
• Generative AI for Smart Consultation (Jul. 2023 – Dec. 2023)
• Multi-category Mapping between Heterogeneous Data (Mar. 2023 – Oct. 2023)
• Trustworthy AI and Causal Inference (Jun. 2022 – Mar. 2024)
• Personalized Fair Job Recommendation Algorithm (Jul. 2022 – Dec. 2022)
• Epidemiological Relevance Evaluation for Vaccination Reactions (Mar. 2022 – Dec. 2023)
• Financial and Telecommunication Data Analysis (Mar. 2022 – May. 2022)

Technical Skills

• Programming Languages: Python, R, C++
• Deep Learning Frameworks: PyTorch, TensorFlow

Additional Information

• Military Service (May. 2015 – Feb. 2017)
  • Auxiliary Police, Army Sergeant (Honorable Discharge)