Email: [email protected]
cv.pdf
Google scholar
LinkedIn
I am a PhD student at the Data Mining Lab in Seoul National University, advised by Professor U Kang.
My research centers on developing self-supervised learning methods that can learn effectively from data with label uncertainty — where labels are incomplete, noisy, or entirely missing. I design algorithms that adapt to diverse data types, including graphs, speech, and temporal sequences, aiming to make robust predictions without relying on large amounts of clean annotations.
These methods have been applied to real-world scenarios such as graph-based classification with incomplete supervision, domain-adapted speech recognition, and forecasting on temporal interaction networks.
Education
Seoul National University (Sep. 2020 - Current)
- Ph.D. Candidate in the Graduate School of Artificial Intelligence
- Expected Graduation: 2026.08
Sung Kyun Kwan University (Mar. 2015 - Aug. 2020)
- B.S in Department of Computer Science and Engineering & Mathematic Science
- GPA: 4.14 / 4.50; C.S.: 4.26 / 4.50
Publications
2025
- PiGLeT: Probabilistic Message Passing for Semi-supervised Link Sign Prediction
Kahyun Park, Junghun Kim, Jinhong Jung, and U Kang
ICDM 2025 [code | pdf | blog (Korean)]
- Accurate Graph-based Multi-Positive Unlabeled Learning via Disentangled Multi-view Feature Propagation
Junghun Kim, Ka Hyun Park, Hoyoung Yoon, and U Kang
KDD 2025 [code | pdf | blog (Korean)]
- Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning
Junghun Kim, Ka Hyun Park, Hoyoung Yoon, and U Kang
AAAI 2025 [code | pdf | blog (Korean)]
Accepted as oral presentation
- Accurate Semi-supervised Automatic Speech Recognition for Ordinary and Characterized Speeches via Multi-hypotheses-based Curriculum Learning
Ka Hyun Park*, Junghun Kim******,* and U Kang (*equal contribution)
PLOS ONE [code | pdf | blog (Korean)]
2024