<aside> 📩 [email protected]
</aside>
<aside> <img 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width="40px" /> @yeonjun-in
</aside>
<aside> <img src="https://prod-files-secure.s3.us-west-2.amazonaws.com/36c7f341-a52a-40e5-a549-3fb4133e27fb/46a01e87-8cf8-4ca8-98b3-f6bc18fbef39/linkedin_480px.png" alt="https://prod-files-secure.s3.us-west-2.amazonaws.com/36c7f341-a52a-40e5-a549-3fb4133e27fb/46a01e87-8cf8-4ca8-98b3-f6bc18fbef39/linkedin_480px.png" width="40px" /> @Yeonjun In
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<aside> <img src="https://prod-files-secure.s3.us-west-2.amazonaws.com/36c7f341-a52a-40e5-a549-3fb4133e27fb/f30304aa-3449-4968-94ed-4711f30b0b92/icons8-twitter-48.png" alt="https://prod-files-secure.s3.us-west-2.amazonaws.com/36c7f341-a52a-40e5-a549-3fb4133e27fb/f30304aa-3449-4968-94ed-4711f30b0b92/icons8-twitter-48.png" width="40px" /> @yeonjun_in
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I’m Yeonjun In, a Ph.D. candidate in the Industrial and Systems Department at KAIST, where I am fortunate to be advised by Prof. Chanyoung Park. I’m actively on research with my best colleagues at Data Science and Artificial Intelligence Lab :ori:.
I work on building reliable and trustworthy AI for diverse real-world applications, including unstructured and structured data. (1) Model Robustness against Data-Centric Issues In real-world applications, diverse data-centric issues hinder the training of robust AI models. My research addresses this challenge by developing: (a) robust representation learning algorithms, and (b) anomaly detection algorithms.
(2) Harmfulness and Safety of LLMs As the use of LLMs continues to grow, their associated safety risks and harmful use cases have become increasingly evident. My research addresses this challenge by (a) uncovering hidden safety risks in current LLMs and (b) mitigating these risks through LLM alignment.
✔️ A paper got accepted at NeurIPS 2025 Workshop: Reliable ML from Unreliable Data. ✔️ A paper got accepted at NeurIPS 2025. ✔️ Two papers got accepted at EMNLP 2025 (1 Main and 1 Findings). ✔️ New preprint: “R1-Act: Efficient Reasoning Model Safety Alignment by Activating Safety Knowledge” ✔️ I received the Outstanding Reviewer (Top 10%) award from KDD 2025 (Aug) and KDD 2025 (Feb).
Full publication list: Google Scholar
(*: joint first author)
R1-ACT: Efficient Reasoning Model Safety Alignment by Activating Safety Knowledge
Yeonjun In, Wonjoong Kim, Sangwu Park, Chanyoung Park
Arxiv. [paper][model][dataset][code]
Training Robust Graph Neural Networks by Modeling Noise Dependencies
Yeonjun In, Kanghoon Yoon, Sukwon Yun, Kibum Kim, Sungchul Kim, Chanyoung Park
NeurIPS 2025. [paper] Collaboration with Adobe Research
