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College of Electronics Information and Applied Science, Kyung Hee University (Global Campus), 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104,
Republic of Korea

Contact Information

Email: yklee@khu.ac.kr

Research Projects

Artificial Intelligence Innovation Hub
Conducting challenging research and development to solve technical and scientific challenges in the AI field based on an open joint research system. - By challenging high-risk and challenging challenges to break through the technical limitations of the AI field, it secures the world's best original technology in the AI field. - Contributing to the improvement of human quality of life by developing AI technology to solve scientific challenges with great economic and social ripple effects. - Universal intelligence machines that can be applied universally regardless of domain to lead the future AI industry.
Development of a distributed graph DBMS for intelligent processing of big graphs
Developing a distributed GDBMS for intelligent high-speed processing of ultra-large graphs. In this task, 1) an intelligent graph application in which three main graph queries (pattern search, analysis, and learning questions) are mixed can be developed on one system, 2) and 3) a cloud-based distributed GDBMS that provides scalable and efficient high-speed query processing for ultra-large graphs is designed and developed. Open source the GDBMS technologies to be developed so that they can be widely used across industries related to graph applications. - Aim to secure the following core technologies to exceed world-class RFP requirements. - Processing distributed disk-based large/dynamic graph queries. - Processing high-speed queries considering worst-optimal join. - Query optimization techniques considering multiple join algorithms and binary join algorithms. - Integrated development technology of three queries using an integrated API library. - Giant graph neural network learning and inference techniques. - RDMA-based high-speed network manager technology. - By securing core technologies, we aim to improve the following six key performance figures by more than 10% to 20% above the RFP target figure, ultimately securing world-class technology. - Improved performance of megaprograph analysis by more than 10% (PageRank: 12 seconds, Triangle Counting: 45 seconds). - Leverage limited computing resources to increase the analytical graph size by more than 50% (1.5 trillion edges). - More than 15% storage throughput per second for time-varying graph data (0.7 billion edges). - More than 10% improvement in incremental processing performance over static processing of graphs (PageRank: 45%, Label Propagation: 12%). - More than 10% improvement in fast graph machine learning processing speed (GCN-1: 0.27 s, GCN-2: 0.72 s, and GCN-3: 0.9 s). - Reduce performance differences by more than 10% between SSD-based query processing and pure memory-based processing (within 4.5%).
Intelligent Interaction for Life Companionship Experience
The center's various projects offer many possibilities in terms of industrial, economic, and technological aspects such as creating new values and providing diversity, presenting new revenue models and contributing to market expansion, strengthening product/business competitiveness, technology ripple effects and availability, and providing new growth engines. - A. Training human resources and strengthening SW capabilities for current employees By training SW working-level experts in the ICBM field, it directly or indirectly contributes to strengthening the technology and business capabilities of companies, and contributes to securing national technical personnel and enhancing technological competitiveness in the mid to long term. - It is the first model in Korea in which a university operates a general graduate school in an industrial complex, and a successful model for joint industry-academia manpower training is presented. - Based on big data, machine learning, mobile, five-sensory recognition, and multimedia technologies, which are the core technologies of the automobile industrial revolution, it is possible to give new values to existing products and present new concept product designs to mobile terminals, robots, drones, and home appliances. It is expected to strengthen the competitiveness of existing industries as well as create next-generation human interaction software projects in new industries such as autonomous driving, education, medical/Bio, defense, manufacturing, and ICT convergence. - Increase utilization in convergence projects between domestic IT industry and other fields - This multimodal sensor-based contextual cognitive technology and knowledge-based decision-making system can be fused with various fields that require software production and personalization services for various purposes, such as smart media and industrial safety, in addition to the field of companionion interaction. - In preparation for the era of the next industrial revolution, we expect to cultivate intellectual engineering professionals who utilize the knowledge of cognitive engineering experts and experts who analyze contexts from various sensor information and systemize them. - Developing working-level SW leaders of small and medium-sized companies leading ICBMs and using them to strengthen corporate SW capabilities and promote and plan competitive projects - Increase cooperation with small and medium-sized enterprises and contribute substantially to management - Use various corporate support policies to resolve corporate issues, upgrade current businesses, and create new businesses. - Contributing to the Industrial Development of Pangyo Area - Establishment of foundation for financing research and projects by continuing expansion of participating companies and contracting companies - Reorganizing membership and giving more benefits to partners and member companies - Strengthen the foundation for the center's operation through corporate R&D support, etc.

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Lab Statistics

193 Journals Published
391 Conferences Published
108 Patents Registered
8 Technical Reports
7 Best Paper Awards
97 Lab Members
14 Active Members
22 Ph.D Alumni

58 Master Alumni

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