Hyoungnyoun Kim


[Curriculum vitae]



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nyoun2 AT gmail DOT com

nyoun2 AT naver DOT com





My research interest is computational intelligence.
I aspire to build an intelligent system that can predict, recall, and associate information from previous experiences and knowledge much like human memory. In order to realize these concepts, I have experienced a gamut of laboratory problems from lower-level research issues including computer vision and image processing to higher-level research work like humanoid robots and a cognitive architecture. I understood the overall structure and correlation between various researches related to the intelligence.


Recently, I have concentrated on a computational associative memory model resembling the human brain. By using the memory model, I tried to retrieve information in large-scale datasets related to contextual human behaviors such as call logs from mobile phones. Moreover, I improved the model to be applied to the content-based image retrieval. I am currently seeking another challenging and intriguing problem.


Ph.D. Dissertation

Incremental Associative Memory Modeling by Extended Hypernetworks for Non-stationary Data, 2015


Research Interest


: Computational Intelligence & Machine Learning

- Neural associative memory; Recognition memory;

- Cognitive architecture;

- Information Retrieval and Recognition;

- Context-awareness;

- Large-scale data mining; Incremental learning;


: Human-Computer Interaction

- Human-robot interaction; Imitation learning; Learning by demonstration;

- Embodied Social Interaction for Family Robot;

- Multimodal sensor network, Natural user interface, 3D interaction


: Computer Vision

- Content-based image retrieval, Augmented reality;



Researches Experience


Computational Intelligence

Brain-like Associative Memory

Extended Hypernetworks

Adaptive User Modeling

Active Video Surveillance System

Human Robot Interaction

Embodied Social Robot

Cooperative Robotic Grasping

Robotic Grasp Learning by Human Demonstration

Human Gesture Spotting for Imitation Learning

Social Game Learning by Imitation

Computer Vision

Fragment-based Image Retrieval

Articulated Object Segmentation

3D Hand Tracking on TableTop

Shape Classification of 3D Objects

Interactive Video Object

Human Computer Interaction

Asymmetric Bimanual Interaction

3D Direct Manipulation

User-centered 3D Remote Interaction

Multimodal User Guide System