Dr. Juyoun Park
Ph.D., Senior Research Scientist
Center for Intelligent & Interactive Robotics
Artificial Intelligence and Robot Institute
Korea Institute of Science and Technology (KIST)
Email: juyounpark[at]kist.re.kr
Biography
I am currently a Senior Research Scentist in the Artificial Intelligence and Robot Institute at Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea. I earned B.S. and Ph.D. degrees in Electrical Engineering at KAIST (Korea Advanced Institute of Science and Technology), Daejeon, Republic of Korea. After finishing my Ph.D., I worked as a Post-doctoral Researcher in the Information & Electronics Research Institute at KAIST. I also worked as a Post-doctoral Scientist in the Department of Biomedical Engineering, School of Engineering and Applied Science (SEAS) at the George Washington University, Washington, DC, United States.
Education
Ph.D. degree
KAIST, Daejeon, Republic of Korea
School of Electrical Engineering
Dissertation: “Online Incremental Classification Resonance Networks for Human-Robot Interaction” (Advisor: Prof. Jong-Hwan Kim)
B.S. degree
KAIST, Daejeon, Republic of Korea
School of Electrical Engineering
Graduated Summa Cum Laude
KAIST Presidential Fellowship (KPF)
Abstract: In human-robot interaction, classification is one of the most important problems, and it is essential particularly when the robot recognizes the surroundings and chooses a reaction based on a certain situation. Each interaction is different since new people appear or the environment changes, and the robot should be able to adapt to different situations during a brief interaction. Thus, it is imperative that the classification is performed incrementally in real time. In this sense, an online incremental classification resonance network (OICRN) is proposed to enable incremental class learning in multi-class classification with high performance online. In OICRN, a scale-preserving projection process is introduced to use the raw input vectors online without a normalization process in advance. Objects can be described in a hierarchical semantics, and people also perceive them in this way. It leads to the need for hierarchical classification in machine learning. Thus, an online incremental hierarchical classification resonance network (OIHCRN) is proposed to enable online incremental class learning in hierarchical classification. By the proposed scale-preserving projection and prior label appending process, OIHCRN reflects the class dependency between class levels and simultaneously normalizes the input vector online. To demonstrate the effectiveness of the proposed networks, experiments are carried out using benchmark datasets. To demonstrate the applicability, OIHCRN is applied to a multimedia recommendation system for digital storytelling. When a digital companion communicates with a user, meaning is delivered effectively by providing appropriate multimedia based on the conversation and the user's context. CNN-OICRN, an integrated network of the Convolutional Neural Network (CNN) for feature extraction and the OICRN for classification, is proposed for model-based online face identification and applied to a robotic system that learns human identities through human-robot interactions. It is verified that the robot can learn the identity of a new user through human-robot interaction and the newly learned knowledge can be reflected in the future interaction.
Academic Services
Editorial Director, Korea Robotics Society (KROS) 2024
Organizing Committee, Korea Robotics Society Annual Conference (KRoC 2024, 2023)
Editorial Committee, Institute of Control, Robotics, and Systems (ICROS) 2024, 2023
Associate Editor, International Conference on Ubiquitous Robots (UR 2024, 2023, 2022, 2021)
Technical Program Committee, The 9th International Conference on Robot Intelligence Technology and Applications (RiTA 2021)
International Program Committee, The 21st International Conference on Control, Automation and Systems (ICCAS 2021)
Invited Talks
“Context-aware artificial intelligence for robot” at Sungkyunkwan University, Apr. 2023
"Context-aware artificial intelligence for real-time robot interaction” at Young Researcher Session, 18th Korea Robotics Society Annual Conference (KRoC 2023), Feb. 2023 [media]
“Artificial Intelligence for Robots” at Seoul National University of Science and Technology, Dec. 2022
“Artificial Intelligence for Robots” at Sungkyunkwan University, Mar. 2022
“Machine Intelligence Learning and its Applications” at Technical University of Denmark, Nov. 2019
“Machine Intelligence Learning and its Applications” at International Workshop, Eurobotics Week 2019, Aalborg University, Nov. 2019
Interdisciplinary Projects
"Natural Replica" Exhibition, Artist View Science (AVS) Project 2023 [media]
"Natural Replica" Exhibition, Artist View Science (AVS) Project 2023
The 18th Korea Robotics Society Annual Conference (KRoC 2023)
International Workshop, Eurobotics Week 2019