Hyoungnyoun Kim

Hyoungnyoun Kim

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Contact

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