🗺️ Daejeon, South Korea
<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
</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
</aside>
<aside> <img src="data:image/png;base64,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" alt="data:image/png;base64,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" width="40px" /> @yeonjun-in
</aside>
I’m Yeonjun In, a Ph.D. student 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.
My main research focuses on mitigating data-centric issues in real-world application to build reliable/trustworthy AI systems. My research topics of interest include, but are not limited to, the following:
<aside> 🗞️ $\textbf{News}$ ✔️ I started a research internship at Adobe Research, USA. ****✔️ A paper got accepted at CVPR 2024. ✔️ A paper got accepted at WWW 2024 Workshop on Data-centric Artificial Intelligence (DCAI). ✔️ Two papers got accepted at WWW 2024. ✔️ A paper got accepted at ICLR 2024.
</aside>
(*: equal contribution)
Noise Robust Graph Learning under Feature-Dependent Graph-Noise
Yeonjun In, Kanghoon Yoon, Sukwon Yun, Kibum Kim, Sungchul Kim, Chanyoung Park
WWW Workshop on Data-Centric AI Oral. 2024. [paper][code]
Weakly Supervised Fine-grained Scene Graph Generation via Large Language Model
Kibum Kim, Kanghoon Yoon, Jaehyeong Jeon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
Seungyoon Choi*, Wonjoong Kim*, Sungwon Kim, Yeonjun In, Sein Kim, Chanyoung Park
Self-Guided Robust Graph Structure Refinement
Yeonjun In, Kanghoon Yoon, Kibum Kim, Kijung Shin, Chanyoung Park
Adaptive Self-training Framework for Fine-grained Scene Graph Generation
Kibum Kim*, Kanghoon Yoon*, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park
Class Label-aware Graph Anomaly Detection
Junghoon Kim, Yeonjun In, Kanghoon Yoon, Junmo Lee, Chanyoung Park
CIKM. 2023 (Short). [paper][code]
Similarity Preserving Adversarial Graph Contrastive Learning
Yeonjun In*, Kanghoon Yoon*, Chanyoung Park
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment
Junseok Lee, Yunhak Oh, Yeonjun In, Namkyeong Lee, Dongmin Hyun, Chanyoung Park
SIGIR. 2022. (Short) [paper][code]
Simple Averaging of Direct and Recursive Forecasts via Partial Pooling using Machine Learning
Yeonjun In, Jae-Yoon Jung
International Journal of Forecasting (2022) (IF. 7.022) The first place in "M5 Forecasting - Accuracy" at Kaggle Competition
2023.08 -
KAIST (Korea Advanced Institute of Science and Technology)
2021.08 - 2023.07
KAIST (Korea Advanced Institute of Science and Technology)
2014.03 - 2021.02
Kyunghee University
2024.04 - 2024.06
Adobe Inc., San Jose, CA, USA
2021.03 - 2021.04
LG CNS, Seoul, Korea
2019.08 - 2020.02
Market Kurly, Seoul, Korea
