Jinkyu Kim is an Assistant Professor of the Department of Computer Science and Engineering at Korea University. He was a research scientist at Waymo (formerly the Google’s self-driving car project), conducting cutting edge research to develop new solutions related to autonomous driving, in particular, to solve outstanding challenges in planning and behavior prediction. He received his Ph.D. in Computer Science from UC Berkeley (advisor: Prof. John Canny) and was part of Berkeley AI Research (BAIR) and Berkeley DeepDrive (BDD). He researched to build explainable and advisable models that can explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future. Currently, He is interested in (1) Image, Text, Sound Multi-modal Representation Learning, (2) Machine Learning for Self-driving Vehicles at Scale, (3) Continual and Life-long Learning, (4)Domain Adaptation and Generalization.

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  • Image, Text, Sound Multi-modal Representation Learning
  • Machine Learning for Self-driving Vehicles at Scale
  • Continual and Life-long Learning
  • Domain Adaptation and Generalization
  • PhD in CS, 2019

    Univ. of California, Berkeley

  • MS in ECE, 2010

    Korea University

  • BS in EE, 2008

    Korea University

Recent Publications

(2024). Bridging the Domain Gap by Clustering-based Image-Text Graph Matching. In ArXiv.

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(2024). Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detection. In ArXiv.


(2024). Robust Sound-guided Image Manipulation. In Neural Networks.

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(2024). EGTR: Extracting Graph from Transformer for Scene Graph Generation. In CVPR2024.


(2024). Higher-order Relational Reasoning for Pedestrian Trajectory Prediction. In CVPR2024.


(2024). Mitigating the Linguistic Gap with Phonemic Representations for Robust Multilingual Language Understanding. In ArXiv.

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(2024). CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-based 3D Object Detection. In AAAI2024.

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(2024). InstructBooth: Instruction-following Personalized Text-to-Image Generation. In ArXiv.

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(2024). BEVMap: Map-Aware BEV Modeling for 3D Perception. In WACV2024.

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(2024). Localization and Manipulation of Immoral Visual Cues for Safe Text-to-Image Generation. In WACV2024.

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Applications and Understanding of Artificial Intelligence (AICE401)


  • Dept. of Computer Science and Engineering, Korea University, Seoul, 02841
  • 605 Woojung Informatics Building
  • Thursday 3:00 to 4:00