Jinwoo Kim

jinwoo-kim [at] kaist.ac.kr

I am a Ph.D. student at KAIST School of Computing, advised by Seunghoon Hong. My name 진우 眞友 is pronounced [jeen-oo] in Korean.

I am interested in endowing current deep learning models with proper inductive biases to enhance generalization and enable learning from limited data. I have been studying this problem primarily in the context of geometric deep learning, focusing on general-purpose deep neural networks that maintain invariances to group symmetries for solving tasks on graphs and structured data.

CV / Google Scholar / GitHub / Twitter / LinkedIn

News

Sep 2024: Simulation-free Neural ODE was accepted to NeurIPS 2024.
Aug 2024: I gave an invited presentation on Probabilistic Symmetrization at Myung Lab @ Sungkyunkwan University.
Jun 2024: Random Walk Neural Networks was accepted to ICML 2024 GRaM Workshop.

Older

Nov 2023: I gave an invited presentation on Probabilistic Symmetrization at Machine Learning Lab @ POSTECH.
Oct 2023: Orbit Distance Minimization was accepted to NeurIPS 2023 NeurReps Workshop.
Sep 2023: Probabilistic Symmetrization was accepted to NeurIPS 2023 as a spotlight presentation.
May 2023: Visual Token Matching was introduced in the latest issue of Nikkei Robotics.
Mar 2023: Visual Token Matching won the ICLR 2023 outstanding paper award, becoming the first paper from South Korea that received the best paper award at major machine learning conferences.
Jan 2023: I gave an invited presentation on Tokenized Graph Transformer at a reading group of Microsoft USA.
Jan 2023: Tokenized Graph Transformer was introduced in a Hugging Face 🤗 blog article on graph machine learning.
Jan 2023: Tokenized Graph Transformer was highlighted as one of the outstanding works on graph transformers in Michael Galkin’s review article on 2022’s graph machine learning.
Jan 2023: Visual Token Matching was accepted to ICLR 2023 as an oral presentation (notable-top-5%) after being ranked #1 in review ratings among the 4,966 submissions.
Jan 2023: I gave an invited presentation on Higher-order Transformers at Qualcomm Research Korea.
Aug 2022: I gave an invited presentation on Tokenized Graph Transformer at Learning on Graphs and Geometry Reading Group (LoGaG).

Research

* denotes equal contribution.

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

Revisiting Random Walks for Learning on Graphs
Jinwoo Kim, Olga Zaghen*, Ayhan Suleymanzade*, Youngmin Ryou, Seunghoon Hong
ICML Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2024
paper / code

Learning Symmetrization for Equivariance with Orbit Distance Minimization
Tien Dat Nguyen*, Jinwoo Kim*, Hongseok Yang, Seunghoon Hong
NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 2023
paper / code / poster

Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
Jinwoo Kim, Tien Dat Nguyen, Ayhan Suleymanzade, Hyeokjun An, Seunghoon Hong
NeurIPS, 2023 (Spotlight Presentation)
paper / code / poster / slides (extended)

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
arXiv, 2023
preprint

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)
paper / code

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)

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

Equivariant Hypergraph Neural Networks
Jinwoo Kim, Saeyoon Oh, Sungjun Cho, Seunghoon Hong
ECCV, 2022
paper / code / poster / slides

Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs
Jinwoo Kim, Saeyoon Oh, Seunghoon Hong
NeurIPS, 2021
paper / code / poster / slides

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

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 / code

Experience

LG AI Research Fundamental Research Lab (FRL)
Research Intern, 2022 (Mentors: Moontae Lee and Honglak Lee)

Korea Advanced Institute of Science and Technology (KAIST)
Research Intern, 2020 (Mentors: Seunghoon Hong and Juho Lee)

Korea Advanced Institute of Science and Technology (KAIST)
Research Intern, 2018-2019 (Mentor: Se-Bum Paik)

Korea Advanced Institute of Science and Technology (KAIST)
Research Intern, 2017 (Mentor: Doyun Lee)

Honors

Outstanding Paper Award, ICLR 2023 (as a coauthor)
Silver Prize, Samsung Humantech Paper Award, 2023 (as a coauthor)
Recipient, Qualcomm Innovation Fellowship Korea, 2022
Excellence Award, KAIST Undergraduate Research Program, 2022 (as a mentor)
Recipient, Kwanjeong Education Foundation Scholarship, 2022-2023
Recipient, KAIST Engineering Innovator Award, 2020 (Top 5 in College of Engineering)
Recipient, National Science & Technology Scholarship, 2018-2020
Recipient, KAIST Alumni Fellowship, 2017-2020
Recipient, KAIST Presidental Fellowship, 2016-2020
Recipient, KAIST Dean's List, Spring 2016 / Fall 2016 / Spring 2018
Recipient, Hansung Scholarship for Gifted Students, 2015-2016

Invited Talks

Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
Aug 2024: Sungkyunkwan University (SKKU)
Nov 2023: Pohang University of Science and Technology (POSTECH)

Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
Aug 2023: KAIST-Samsung Electronics DS Division Exchange Meetup

Pure Transformers are Powerful Graph Learners
Jan 2023: Microsoft USA
Nov 2022: NeurIPS 2022 at KAIST
Aug 2022: Learning on Graphs and Geometry Reading Group (LoGaG)

Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs
Jan 2023: Qualcomm Korea
Jan 2022: KAIST AI Workshop 21/22
Dec 2021: NeurIPS Social: ML in Korea

SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Sep 2021: Naver AI Author Meetup
Sep 2021: Korean Conference on Computer Vision (KCCV)

Spontaneous Retinal Waves Can Generate Long-Range Horizontal Connectivity in Visual Cortex
Oct 2019: Society for Neuroscience (SfN)

Academic Services

Conference Reviewer, AISTATS 2025, ICLR 2025, NeurIPS 2022 - 2024, ICML 2023 - 2024, LoG 2022 - 2024, ICML GRaM Workshop 2024, CVPR 2022, ACCV 2022
Journal Reviewer, TMLR 2024, Neural Networks 2023

Teaching

Teaching Assistant, Computer Vision (CS576), Spring 2022 / 2023
Teaching Assistant, Introduction to Deep Learning (CS492I / CS371), Fall 2021 / 2022 / 2023
Teaching Assistant, Samsung Research AI Expert Program, Summer 2021 / 2022 / 2023
Teaching Assistant, Undergraduate Research Program (URP), Spring 2022 / 2024
Teaching Assistant, School of Computing Colloquium (CS966 / CS986), Spring 2021

Music

I love listening to and making music! My favorite musicians include Lamp, Radiohead, and Ryuichi Sakamoto. Below are some of my original compositions:

Last updated: Oct 2024

Built from Jon Barron's academic website