Junsu Kim

About

I am a Ph.D. student at ALIN Lab advised by Prof. Jinwoo Shin and Prof. Kimin Lee at Korea Advanced Institute of Science and Technology (KAIST), and a visiting Ph.D student at UC Berkeley, advised by Prof. Sergey Levine. Previously, I received B.S in Electrical Engineering from KAIST in Feb. 2020. My main research question is to solve challenging decision making problems via RL (e.g., long-horizon tasks, robotic manipulation). Relevant topics include hierarchical RL and representation learning for RL. I am also broadly interested in diverse areas of reinforcement learning, such as goal-conditioned RL, model-based RL, and multi-agent RL. Prior to that, I worked on machine learning for drug discovery.

Topics of interest

• Deep Reinforcement Learning: ICML'23, ICLR'23, NeurIPSW'22, ICML'22, NeurIPS'21
• Representation learning for Reinforcement Learning ICML'24, ICML'23
• Machine Learning for Drug Discovery: TMLR'24, ICML'21, NeurIPS'20

Publications

(*: Equal contribution, C: Conferences, J: Journal, W: Workshop, P: preprint).

[P1] Unsupervised-to-Online Reinforcement Learning

Junsu Kim*, Seohong Park*, Sergey Levine

Preprint

Paper

[C7] Visual Representation Learning with Stochastic Frame Prediction

Huiwon Jang, Dongyoung Kim, Junsu Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo

International Conference on Machine Learning (ICML), 2024

Paper Code Website

[C6] Multi-View Masked World Models for Visual Robotic Manipulation

Younggyo Seo*, Junsu Kim*, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel

International Conference on Machine Learning (ICML), 2023

RSS Workshop on Experiment-oriented Locomotion and Manipulation Research, 2023 (Spotlight Presentation)

Paper Code Website

[J1] Holistic Molecular Representation Learning via Multi-view Fragmentation

Seojin Kim*, Jaehyun Nam*, Junsu Kim, Hankook Lee, Sungsoo Ahn, Jinwoo Shin

Transactions on Machine Learning Research (TMLR), 2024

ICLR Workshop on Machine Learning for Materials, 2023

Paper

[C5] Imitating Graph-Based Planning with Goal-Conditioned Policies

Junsu Kim, Younggyo Seo, Sungsoo Ahn, Kyunghwan Son, Jinwoo Shin

International Conference on Learning Representations (ICLR), 2023

Paper Code

[W1] Dynamics-Augmented Decision Transformer for Offline Dynamics Generalization

Changyeon Kim*, Junsu Kim*, Younggyo Seo, Kimin Lee, Honglak Lee, Jinwoo Shin

NeurIPS Workshop on Offline Reinforcement Learning, 2022

Paper

[C4] Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning

Kyunghwan Son, Junsu Kim, Sungsoo Ahn, Roben Delos Reyes, Yung Yi, Jinwoo Shin

International Conference on Machine Learning (ICML), 2022

Paper

[C3] Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning

Junsu Kim, Younggyo Seo, Jinwoo Shin

Neural Information Processing Systems (NeurIPS), 2021

Paper Slides Poster Code

[C2] Self-Improved Retrosynthetic Planning

Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin

International Conference on Machine Learning (ICML), 2021

Paper Slides Poster Code

[C1] Guiding Deep Molecular Optimization with Genetic Exploration

Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin

Neural Information Processing Systems (NeurIPS), 2020

Paper Code

Education

Honors & Awards

Invited Talks

Academic Services

Vitæ

Full CV in PDF (Last update: Oct 2024).

• UC Berkeley Feb. 2024 - Jul. 2024

  Visiting Student (Advisor: Sergey Levine)
  
• KAIST Mar. 2020 - present

  M.S./Ph.D. in Artifical Intelligence
  
• AIZEN global Mar. 2018 - Aug. 2018

  Intern, Deep Learning for Financial Service
  
• SK Hynix Jun. 2017 - Jul. 2017

  Intern, DRAM Computing Core Design
  
• National University of Singapore Jan, 2017 - May. 2017

  Visiting Student
  
• KAIST Mar, 2015 - Feb. 2020

  B.S. in Electrical Engineering