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. 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 [P1, W3].
Presently, my focus lies in the retrieval and integration of external structured data into language models. Specifically, I am exploring how diverse data types, beyond texts, can enhance the factuality and interpretability of language models. Plus, I am working on the application of machine learning models where domain knowledge is crucial such as medicine.
Recent Publications (See all)
Dongkwan Kim and Alice Oh. "Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning", 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
Jooyeon Kim, Dongkwan Kim, and Alice Oh. "Homogeneity-Based Transmissive Process To Model True and False News in Social Networks", International Conference on Web Search and Data Mining (WSDM), 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), 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