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Jinwoo Kim jinwoo-kim [at] kaist.ac.kr I am a Ph.D. student at KAIST advised by Seunghoon Hong and a visiting scholar at NYU working with Kyunghyun Cho. My name 진우 眞友 is pronounced [jeen-oo] in Korean. I am interested in making current deep learning models generalize better out of their training data so that they can be used to solve challenging problems, such as those in scientific domains. I have been studying this problem primarily from the viewpoint of geometric deep learning, focusing on all-purpose deep neural nets that can reliably reason upon novel transformed inputs. I often use tools from theories of graphs, (semi)groups and manifolds, and Markov processes such as random walks, diffusions and flows. CV / Google Scholar / GitHub / X / LinkedIn |
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Experience
New York University
LG AI Research |
Selected Publications
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Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim*, Xingyue Huang*, Krzysztof Olejniczak, Kyungbin Min, Michael Bronstein, Seunghoon Hong, İsmail İlkan Ceylan Preprint, 2025 paper / code |
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Sequence Modeling with Spectral Mean Flows
Jinwoo Kim, Max Beier, Petar Bevanda, Nayun Kim, Seunghoon Hong NeurIPS, 2025 paper / code |
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Revisiting Random Walks for
Learning on Graphs
Jinwoo Kim, Olga Zaghen*, Ayhan Suleymanzade*, Youngmin Ryou, Seunghoon Hong ICLR, 2025 Spotlight Presentation (3%); ICML GRaM Workshop, 2024 paper / code / poster |
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Learning Probabilistic
Symmetrization for Architecture Agnostic
Equivariance
Jinwoo Kim, Tien Dat Nguyen, Ayhan Suleymanzade, Hyeokjun An, Seunghoon Hong NeurIPS, 2023 Spotlight Presentation (3%) paper / code / poster / slides / extended slides |
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Universal Few-shot Learning
of Dense Prediction Tasks with Visual Token Matching
Donggyun Kim, Jinwoo Kim, Seongwoong Cho, Chong Luo, Seunghoon Hong ICLR, 2023 Outstanding Paper Award (0.08%) paper / code |
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Pure Transformers are
Powerful
Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong NeurIPS, 2022 paper / code / talk / poster / slides / extended slides |
Full Publications
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Inverting Data Transformations via Diffusion Sampling
Jinwoo Kim*, Sékou-Oumar Kaba*, Jiyun Park, Seunghoon Hong, Siamak Ravanbakhsh Under review, 2025 |
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Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim*, Xingyue Huang*, Krzysztof Olejniczak, Kyungbin Min, Michael Bronstein, Seunghoon Hong, İsmail İlkan Ceylan Preprint, 2025 paper / code |
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Sequence Modeling with Spectral Mean Flows
Jinwoo Kim, Max Beier, Petar Bevanda, Nayun Kim, Seunghoon Hong NeurIPS, 2025 paper / code |
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Revisiting Random Walks for
Learning on Graphs
Jinwoo Kim, Olga Zaghen*, Ayhan Suleymanzade*, Youngmin Ryou, Seunghoon Hong ICLR, 2025 Spotlight Presentation (380/11672=3.26%); ICML GRaM Workshop, 2024 paper / code / poster |
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3D Denoisers are Good 2D
Teachers: Molecular Pretraining via Denoising and
Cross-Modal Distillation
Sungjun Cho, Dae-Woong Jeong, Sung Moon Ko, Jinwoo Kim, Sehui Han, Seunghoon Hong, Honglak Lee, Moontae Lee AAAI, 2025 Oral Presentation paper |
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Simulation-Free Training of
Neural ODEs on Paired Data
Semin Kim*, Jaehoon Yoo*, Jinwoo Kim, Yeonwoo Cha, Saehoon Kim, Seunghoon Hong NeurIPS, 2024 paper / code |
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Learning Symmetrization for
Equivariance with Orbit Distance Minimization
Tien Dat Nguyen*, Jinwoo Kim*, Hongseok Yang, Seunghoon Hong NeurIPS NeurReps Workshop, 2023 paper / code / poster |
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Learning Probabilistic
Symmetrization for Architecture Agnostic
Equivariance
Jinwoo Kim, Tien Dat Nguyen, Ayhan Suleymanzade, Hyeokjun An, Seunghoon Hong NeurIPS, 2023 Spotlight Presentation (378/12345=3.06%) paper / code / poster / slides / extended slides |
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Universal Few-shot Learning
of
Dense Prediction Tasks with Visual Token
Matching
Donggyun Kim, Jinwoo Kim, Seongwoong Cho, Chong Luo, Seunghoon Hong ICLR, 2023 Outstanding Paper Award (4/4955=0.08%) paper / code |
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Pure Transformers are
Powerful
Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong NeurIPS, 2022 paper / code / talk / poster / slides / extended slides |
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Transformers Meet Stochastic
Block Models: Attention with Data-Adaptive Sparsity
and Cost
Sungjun Cho, Seonwoo Min, Jinwoo Kim, Moontae Lee, Honglak Lee, Seunghoon Hong NeurIPS, 2022 paper / code / poster |
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Equivariant Hypergraph Neural
Networks
Jinwoo Kim, Saeyoon Oh, Sungjun Cho, Seunghoon Hong ECCV, 2022 paper / code / poster / slides |
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Transformers Generalize
DeepSets and Can be Extended to Graphs and
Hypergraphs
Jinwoo Kim, Saeyoon Oh, Seunghoon Hong NeurIPS, 2021 paper / code / poster / slides |
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SetVAE: Learning Hierarchical
Composition for Generative Modeling of
Set-Structured
Data
Jinwoo Kim*, Jaehoon Yoo*, Juho Lee, Seunghoon Hong CVPR, 2021 paper / code / project page / poster / slides |
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Spontaneous Retinal Waves Can
Generate Long-Range Horizontal Connectivity in
Visual
Cortex
Jinwoo Kim*, Min Song*, Jaeson Jang, Se-Bum Paik The Journal of Neuroscience 40(34), 2020 paper |
Honors
Outstanding Researcher Award,
KAIST-Mila Prefrontal AI Research Center, 2024 |
Invited Talks
Architecture-Agnostic Invariances for Deep Learning
Universal Few-shot Learning of Dense
Prediction
Tasks with Visual Token Matching
Pure Transformers are Powerful Graph
Learners
Transformers Generalize DeepSets and Can be
Extended to Graphs and Hypergraphs
SetVAE: Learning Hierarchical Composition for
Generative Modeling of Set-Structured Data
Spontaneous Retinal Waves Can Generate
Long-Range Horizontal Connectivity in Visual
Cortex |
Academic Services
Conference Reviewer, AISTATS 2025–2026,
ICLR 2025–2026, NeurIPS 2022–2025, ICML 2023–2025,
LoG 2022–2025, TAG-DS 2025, IJCNN 2025, ICCV 2025 SP4V Workshop,
ICML 2024 GRaM Workshop, CVPR 2022, ACCV 2022
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Teaching
Teaching Assistant, Computer Vision
(CS576), Spring 2022 / 2023 |
MusicI love listening to and making music! My favorite musicians include Lamp, Radiohead, and Ryuichi Sakamoto. Below are some of my original compositions: |
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I occasionally post music stuff on my blog. |
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Built from Jon Barron's academic website |