Research Labs

From “Small computing” such as mobile and embedded system to “Big computing” such as large-scale data center, modern computer system has evolved to suit a variety of socially demanding computing environments. High Performance Computing Lab focuses on exploring a variety of research into memory-based next-generation computer architecture and systems for rapidly changing computing environments. Recently, in order to solve the performance gap between processor-memory-storage, which is getting worse due to the use of memory/data-centric applications such as big data processing, artificial intelligence, etc., we conduct research on the new memory/storage systems and intelligent data management techniques.

• Intelligent Hybrid Memory Systems
• Data-centric Computing for Big-data Processing & Artificial intelligent
• Memory-storage Integrated Systems with Next-generation Non-volatile Memory

My current researches can be divided as below. The first issues is the next-generation internet services. The internet has the potential for new innovative services. Facebook and you tube are examples of such services. Developing new service models and their supporting system architectures are challenging tasks. The second issues are mobile and ad hoc network technologies including sensor networks. For ubiquitous societies, all devices will be connected via various wireless networks. In these environments, efficient network protocols and algorithms such as scheduling disciplines are important. The third issues are mobile applications including game applications. In the near future, apps will be dominating applications executing in iPhone and Android platforms. In these platforms, new innovating apps are important in both educational and commercial perspectives. Next-Generation Internet

• Internet Service Architecture
• Mobile Networks
• Ad hoc Networks
• System Software
• Computer Security
• Mobile Games

Our research group is interested in developing machine learning and data mining techniques to analyze various types of massive data frequently occurring from real-world events. Real-world complex systems are represented by relationships between entities as in social networks, knowledge graphs, user-item networks, molecular graphs, biomedical networks, etc. We work on designing models that effectively analyze such real-world data to solve interesting applied problems closely related to our lives.

• Graph machine learning
• Large-scale graph analytics
• Applied data science

Our research topic is visual computing, which is a technology for handling images and 3D models such as computer graphics, image processing, visualization, computer vision and virtual reality. We aim to solve existing problems in the industrial field by utilizing visual computing technology. Also, we study implementation and application technologies for digital twin, which is the key of the fourth industrial revolution. Currently, we are focusing on the generation and recognition of geometrical shape through 3D reconstruction, and real-time collaborative visualization method using commercial game engine and WebGL.

• As-is design reconstruction technology based on 3D scan
• 3D map generation technology
• Collaborative visualization technology based on virtual reality

Our primary goal in the Software Engineering Laboratory is to apply software engineering technology to real world engineering problems. Currently researches; computer vision, parallel processing and bioinformatics are moving forwards in our lab. The first subject is to develop an intelligent surveillance system including smart CCTVs. Our goal is to recognize moving objects: persons or vehicles and to track each object along with their trajectories. Recognition of a face, a type of gesture and vehicle models are key techniques in this fields. The other area is to apply SWE technique to bioinformatics areas. Examples include functional MR imaging(fMRI) and brain waves. FMRI and brain wave analyses require extensive computational techniques. Development of analysis models is a hot area besides clinical fMRI analysis for mental disorders. Parallel processing is inevitable to execute these areas in real-time. We are also interest in collaboration with medical science. Furthermore, we try to converge parallel processing, image processing and IT technology into future technology.

• Intelligent surveillance system
• Deep learning based object recognition
• High performance computing technology
• Parallel algorithm development
• Functional magnetic resonance imaging
• Electroencephalograph analysis
• Brain-computing Interface

The Software and Interaction Technologies Lab research focuses on usability, user behavior tracking and modeling, user action representation, and user requirements elicitating. Our main research topic is analysis, modeling, and reproduction of user behavior. Currently, we are conducting research to discover and solve problems for a better user experience in VR.

• Usability testing
• >User requirements analysis
• Usability in VR

AI-assisted applications such as robots, self-driving vehicles, automatic diagnosis from medical images, and image/video understanding are now gaining more and more attractions. Computer vision is a key research area for the aforementioned applications. Thanks to the recent tremendous advance of machine learning known as deep learning, computer vision has been improved rapidly and its evolution is still underway. Vision and Learning Lab, part of the Division of Computer Science and Engineering in Jeonbuk National University, is currently working on various research topics as follows. We welcome prospective students who are highly motivated and talented to perform cutting-edge research.

• Visual tracking, video understanding
• Explainable AI
• Medical image analysis

With the recent development of technologies such as big data and IoT, the requirements of the security for personal information are rapidly increasing. Cryptography is one of the fundamental areas of the security and privacy, and if cryptography is properly used, then the privacy can be theoretically guaranteed. Instead, we will not be able to use the data or lose all efficiency. Current cryptographic primitives overcome these issues through various techniques of optimization, but sometimes such optimizations yield vulnerabilities. The cryptography lab focuses on a cryptanalysis of the primitives and cryptographic hardness problems, and aims to develop new cryptographic primitives.

Operating system is a basis of computer science and engineering. OS provides the environments that platform SWs and user applications are running. Recently, Cloud computing based on virtualization technology leads a revolution about the computing experiences of enterprises and users. To contribute the development, OS laboratory currently working on following research subjects.

• Cloud technologies: VM migration, Educational Cloud service
• IO Virtualization: storage and network devices

Our main research topic is to provide some means to detect and/or protect malicious attacks, especially in wireless environment as WLAN. It is ranged from the hardware based methods such as radio fingerprinting to the logical and protocol methods such as cryptographic algorithm. In the vertical spectrum, it includes key management protocols, trust relationship evaluation, anonymity means to improve user privacy, and advanced methods for protecting DDoS. Another topics we are interesting these days are to establish a security framework for enabling security critical service on open cloud environment, and to design and implement a set of graphic interfaces for DDS(Data Distribution Service) system.

• Wireless and/or mobile security issues
• Routing and key management on MANET
• Security framework for open cloud
• Trust management
• Distributed data services
• Privacy preserving methods

Cognitive computing is the simulation of human thought processes in a computerized model, which involves self-learning systems based on data mining, machine learning, pattern recognition, and natural language processing to mimic the way the human brain works. Cognitive computing systems help human experts use big data to make better decisions by enhancing human’s cognitive performance and strengthening human’s domains-specific knowledge. Cognitive computing requires us to integrate many AI-related areas such as big data analytics, natural language processing, machine learning, and pattern recognition, which are all the core technologies for the Smart Machine era.

• Natural language processing
• Information retrieval
• Deep learning
• Machine learning

Our researches are on social data analysis and clinical causal relation detection. The first topic is a social event detection based on timeline and sentiment analysis on social media. The second topic is a disputant relation-based classification for contrasting opposing views of contentious news issues. Providing a classified view of the opposing views of the issues can help readers to easily understand the issue from multiple perspectives. The third issue is a clinical problem-action relation detection. It is based on clinical semantic units and event causality patterns in order to present a chronological view of a patient’s problem and a physician’s action.

• Information Retrieval
• Text Classification
• Medical Data Mining
• Social Network Analysis
• Media Bias Detection
• Cross-lingual Event Detection and Tracking

My current researches can be divided as below. The first issue is the ontology based information retrieval (IR). Ontology is used as a knowledge base that makes concept based IR possible. The second issue is ontology based question answering system whose interface provides natural language queries. It is broadly accepted as the next generation intelligent IR interface. Once user intention buried in the query through semantic analysis, our inference exploiting ontology can extract exact answer which fully satisfies users. The third issue is how to make it possible to perform context aware social curation. Social curation is an effective way of obtaining proper information with the help of social network services. My final issue is to develop a web social service platform. The previous three issues are realized on top of this platform.

• Ontology
• >Genome read ma
• Conceptual Modeling
• Fuzzy databases
• Uncertainty
• Social system

The lab are mainly focusing on the study of computer vision and pattern recognition technologies and their applications. Current research and development include text detection from natural images, plant species identification via leaf image recognition, and defect detection. Education of computer vision for undergraduate students is another works led by the lab. Especially motivating undergraduates using OpenCV is a good strategy for the education.

• Pattern recognition
• >Computer vision
• Education of computer vision for undergraduates