About
I am a Ph.D. student at the KAIST School of Computing, advised by Alice Oh. My research interests are in learning representations of structured and unstructured knowledge using GNNs and LLMs. In particular, I have focused on (1) leveraging the inherent structures (e.g., edges and subgraphs) to learn graph-structured data [C2, C3] and (2) designing a new data structure for efficient representation learning of subgraphs [C4].
My ongoing research focuses on the intersection of Graph and Language Models, including LLMs/GNNs on multi-culture awareness, single-cell RNA-seq data, logical validity, and multi-turn dialogue evaluation.
Recent Publications (See all)
Dongkwan Kim and Alice Oh. "Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning", International Conference on Machine Learning (ICML), 2024
Chani Jung, Dongkwan Kim, Jiho Jin, Jiseon Kim, Yeon Seonwoo, Yejin Choi, Alice Oh, Hyunwoo Kim. "Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models", Arxiv, 2024
Dongkwan Kim, Jiho Jin, Jaimeen Ahn and Alice Oh. "Models and Benchmarks for Representation Learning of Partially Observed Subgraphs", International Conference on Information and Knowledge Management (CIKM, Short Papers Track), 2022
Dongkwan Kim and Alice Oh. "Efficient Representation Learning of Subgraphs by Subgraph-To-Node Translation", Workshop on Geometrical and Topological Representation Learning at ICLR (ICLR GTRL), 2022
Dongkwan Kim and Alice Oh. "How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision", International Conference on Learning Representations (ICLR), 2021
Dongkwan Kim and Alice Oh. "Supervised Graph Attention Network for Semi-Supervised Node Classification", Workshop on Graph Representation Learning at NeurIPS (NeurIPS GRL), 2019
Yeon Seonwoo, Sungjoon Park, Dongkwan Kim, and Alice Oh. "Additive Compositionality of Word Vectors", Workshop on Noisy User-generated Text at EMNLP (EMNLP W-NUT), 2019
Education
- M.S. School of Computing, KAIST, Sep 2019
- B.S. Major in Computer Science and Minor in Chemistry, KAIST, Feb 2018
Talks
Dongkwan Kim. "Leveraging Structure for Graph Neural Networks", IBS Data Science Group Seminar, 2022
Dongkwan Kim. "How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision", Learning on Graphs and Geometry Reading Group (LoGaG), 2021
Academic Services
- Reviewer: ICLR (2020, 2022, 2024), GRL+ Workshop (2020), ACL ARR (2021), GTRL Workshop (2022), NeurIPS (2022, 2023, 2024), LoG (2022, 2023), ICML (2023), GLB Workshop (2023)
- Student volunteer: ICLR Social ML in Korea (2020), ICLR (2021), NeurIPS 2022 at KAIST (2022), NYU-KAIST Talk Series on LMs (2023)
- Organizer: KAIST AI Workshop (21/22)
- Contributor: KAIST ILP Tech (March 2022)
Teaching Experiences
- TA, Head TA of Data Structure (Spring 2018, Fall 2018)
- Head TA, TA of Machine Learning for Natural Language Processing (Fall 2019, Spring 2021), Best TA Award at Fall 2019
- Head TA of Deep Learning for Real-world Problems (Spring 2020, Fall 2020), Best TA Award at Spring 2020