Chanyoung Park @ KAIST

Professor

I am an assistant professor in the Department of Industrial Engineering and Systems and also affiliated with the Graduate School of AI at KAIST.
Before joining KAIST, I was a postdoctoral research fellow in Computer Science at University of Illinois at Urbana-Champaign. I obtained my Ph.D degree in Computer Science and Engineering from Pohang University of Science and Technology (POSTECH), and B.S. in Computer Science from Sogang University.

Data-Centric AI, Model-Centric AI, Multimodal Data Mining, AI for Science
- Mining meaningful knowledge from multimodal data to develop artificial intelligence solutions for various real-world applications across different disciplines.
- Application domains: Recommendation system, AI for Science (Materials science, Chemistry, Bioinformatics), Large Language Model applications, Scene understanding, Social network analysis, Medical image analysis, Fraud/Anomaly detection, Knowledge graph, Sentiment analysis, Purchase/Click prediction, Time-series and spatio-temporal analysis, etc.

I am actively seeking for passionate and self-motivated Ph.D. students, MS students, and postdocs. If you are interested, send me an email with your interests, CV, and transcript.
I am also looking for undergraduate students who are interested in doing internship in our lab, which will be open every summer and winter. If you are interested, send me an email with your interests, CV, and transcript.
- Interns will get opportunities to study about research topics that our lab is focusing on, and participate in research projects with graduate students.
- If you are interested in joining our lab as a graduate student, I strongly recommend you to do an internship before you apply.
There are three ways to join our lab. You can apply through 1) Department of Industrial Engineering and Systems or 2) Graduate School of Data Science or 3) Graduate School of AI.

KAIST (Korea Advanced Institute of Science and Technology), Daejeon, South Korea
- Assistant Professor (2020.11 - Present)
University of Illinois at Urbana-Champaign, IL, USA
- Postdoctoral Research Fellow in Computer Science Department (2019.3 - 2020.10)
- Host: Prof. Jiawei Han
Microsoft Research, Beijing, China
- Research Intern in Social Computing Group (2017.9 - 2017.12)
- Host: Dr. Xing Xie
NAVER, Seongnam, South Korea
- Research Intern in NAVER Shopping (2017.3 - 2017.6)
- Host: Dr. Jung-Tae Lee

POSTECH (Pohang University of Science and Technology)
- Ph.D. in Computer Science and Engineering (2019)
- Dissertation: Recommendation Framework via Matrix Factorization and Translation
- Advisor: Prof. Hwanjo Yu
Sogang University
- B.S. in Computer Science and Engineering (2014)
- Graduated with Honors (Magma Cum Laude)

KSE801: Recommender System and Machine Learning on Graphs
- Fall 2021, Fall 2022, Fall 2023
CoE202: Basics of Artificial Intelligence
- Fall 2021
IE343: Statistical Machine Learning
- Spring 2021, 2022, 2023, 2024
DS503: Machine Learning for Data Science
- Spring 2023, 2024
KSE527: Deep Learning
- Spring 2022

Best Paper Award, KDD 2024 Workshop on Federated Learning for Data Mining and Graph Analytics (FedKDD) (2024)
Best Paper Award, KDD 2024 Workshop on Human-Interpretable AI (2024)
Gold Prize, 30th Samsung Humantech Paper Award (2024)
Technology Innovation Awards of College of Engineering, KAIST (2023)
Top Reviewer of NeurIPS 2023
Best Paper Award, ICML 2023 Workshop on Computational Biology (2023)
KAIST Excellence in Teaching Award of Year 2022, KAIST (2023)
Best Teaching Award, ISysE KAIST (2022)
ICDM 2018 Travel Award (2018)
Award of excellence, Microsoft Research Asia Internship Program, Beijing, China (2017)
Naver Ph.D Fellowship (2016)
Top 1.1% in RecSys Challenge (2015)

Learning on Graph and its Application to Biomedical Data [Slide] @ 2024 한국인공지능학회 동계단기강좌
그래프 신경망 기반 화학/소재분야 연구 동향 (Out-of-Distribution 문제를 중심으로) [Slide] @ 삼성전자 종합기술원, 삼성전자 DS
Universal User Representation Learning based on Continual Learning [Slide] @ NAVER Techtalk
그래프 신경망 기반 화학/소재분야 연구 동향 [Slide] @ KCC 2023, 메릭웨비나
Representation Learning on Graphs [Slide] @ IBS Data Science Group
Recent Advancement in Machine Learning on Graph 2 [Slide] @ 2022 데이터베이스 소사이어티 여름학교
Recent Advancement in Machine Learning on Graph 1 [Slide] @ 2021 한국인공지능학회 동계단기강좌, KRICT, POSTECH CSE, Sogang CSE, 2021 대한산업공학회 춘계학술대회, ETRI, NewDB, SeoulTech Data Science, APCTP, IEEE BigComp 2022 Tutorial
Graph-based Multi-aspect User Behavior Analysis @ KCC, Korea Military Academy, KJDB

Regular PC Member/Reviewer in KDD, NeurIPS, ICLR, ICML, AAAI, WWW, SIGIR, WSDM, IJCAI, CIKM, LoG, ECML-PKDD, BigData, etc
Area Chair in KDD, ICLR, NeurIPS, ICML (Since 2024)
Area chair in ICML 2024 AI4Science Workshop, NeurIPS 2023 AI4Science Workshop

Proceedings Chair, The ACM International Conference on Information and Knowledge Management (CIKM) [2023]