Faculty Research Profile
Yeon-Chang Lee
학력
- 2021: Ph.D., Computer Science, Hanyang University, Korea
- 2014: B.S., Medical Information Technology, Eulij University, Korea
주요 경력
- 2023 - Present: Assistant Professor, UNIST, Korea
- 2022 - 2023: Postdoctoral Researcher, Georgia Institute of Technology, USA
- 2021 - 2022: Postdoctoral Researcher, Hanyang University, Korea
수상/학회/외부활동
- 2022 - 2023: Postdoctoral Fellowship (Overseas Track), National Research Foundation of Korea
- 2021: Best Ph.D. Dissertation Award, Research Institute of Industrial Science, Hanyang University
- 2018: AAAI Student Scholarship Award
- 2017: NAVER Ph.D. Fellowship
데이터 인텔리전스 연구실
Data Intelligence Lab
데이터 인텔리전스 연구실은 다양하고 복잡한 실세계 데이터를 효과적으로 수집, 분석, 활용하는 데 중점을 두고 있습니다. 주요 연구 주제로는 데이터 마이닝, 그래프 머신러닝, 네트워크 과학, 추천 시스템 등이 있습니다. 본 연구를 통해 현실 세계의 문제를 해결하고 사회의 삶의 질을 향상시키는 데 도움이 될 수 있는 "정확하고 신뢰할 수 있는 데이터 마이닝 방법론"을 개발하고자 합니다.
Data Intelligence Lab focuses on collecting, analyzing, and utilizing diverse and complex real-world data effectively. Our major research topics include data mining, graph machine learning, network science, and recommendation systems. Our goal is to develop "accurate and trustworthy data mining methodologies," which can help solve real-world problems and enhance the quality of life in society.
Data Intelligence Lab focuses on collecting, analyzing, and utilizing diverse and complex real-world data effectively. Our major research topics include data mining, graph machine learning, network science, and recommendation systems. Our goal is to develop "accurate and trustworthy data mining methodologies," which can help solve real-world problems and enhance the quality of life in society.
연구분야
데이터 마이닝, 그래프 머신 러닝, 네트워크 과학, 추천 시스템 (Data Mining, Graph Machine Learning, Network Science, Recommender Systems)
Data Mining, Graph Machine Learning, Network Science, Recommender Systems
연구 희망분야
정확하고, 강건하며, 공정한 그래프 머신 러닝 및 이를 활용한 어플리케이션 (Accurate, Robust, and Fair Graph Machine Learning and Its Applications)
Accurate, Robust, and Fair Graph Machine Learning and Its Applications
연구주제
그래프 표현 학습, 신뢰할 수 있는 그래프 마이닝, 추천 시스템, 그래프 + X (커리어 모델링, 이상 탐지 등)
Graph Representation Learning, Trustworthy Graph Mining, Recommender Systems, Graph + X (Career Modeling, Anomaly Detection, etc)
국가연구개발사업 기술 분류체계
국가과학기술표준분류
EE. 정보/통신 > EE01. 정보이론 > EE0108. 인공지능
논문
- The ACM Web Conference (WWW), Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding, Hyunsik Yoo / Yeon-Chang Lee / Kijung Shin / Sang-Wook Kim, (2023.05)
- ACM International Conference on Information & Knowledge Management (CIKM), MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation, {Taeri Kim* / Yeon-Chang Lee*} / Kijung Shin / Sang-Wook Kim, (2022.10)
- ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks, {Wonchang Lee* / Yeon-Chang Lee*} / Dongwon Lee / Sang-Wook Kim, (2021.07)