- Natural Sciences > Department of Computer and Information Science (Department of Computer and Information Science)
The major of computer and information science is separated into Intelligent Computer System, Information–Communication Convergence(ICT Convergence), DB and Data Mining, Software Engineering , Security and Parallel Algorithm, Network Management, Intelligent Cyber-physical Systems and Internet of Things.
Intelligent Computer System Laboratory emphasize and highlight collaborative research and group partnerships. More than 30 alumni members of ICSL are working at important positions in many fields after graduation, and now graduate students pursuing PhD and master degree study eagerly under the advise of Professor, In-Jeong Chung.
The ICT Convergence Laboratory researches information-communication convergence technologies that provide services by connecting IT and virtual worlds such as Mobile, IoT,Bigdata,Cloud, AI,MR,Wearable and security technologies and physical world like energy, transportation, environment, smart factory and smart city.
DB and Data Mining Laboratory is the process of discovering interesting (non-trivial, implicit, previously unknown, and potentially useful) patterns or knowledge from large amounts of data. We are interested in the development of algorithms and a wide range of applications in the area of intelligent database and data mining.
In Software Engineering Laboratory, Well-defined models can substantially improve the development and evolution of complex, multi-platform, and long-running software systems. Software models play a pivotal role particularly for component-, framework-, and product line-based development. Modeling expertise requires both domain knowledge software knowledge. Software modeling disciplines are rapidly accumulating in terms of languages, codified expertise, reference models, and automated tools.
In Security and Parallel Algorithm Laboratory, to increase energy efficiency, the computer architectural approach controlling the frequency and/or the number of cores at hardware/software level, and the compression approach at algorithmic level have been proposed. In the computer architectural approach, there is a tradeoff between power consumption and execution time. That is, if we increase the frequency, the power consumption is increased while the execution time is decreased. In the image compression or information security, we can improve the performance efficiency at algorithmic level and/or derive the optimal applications parameter based on the tradeoff between the video/security quality and the energy consumption. We are researching the analysis of machine characteristics and parallel applications(multimedia/crypto/image processing algorithms) characteristics collectively, and thus improve the energy efficiency of compression using a commercial multi-core platform.
Network Management Laboratory engages in a wide range of experimental and practical research in the area of network and systems management. The goal is to develop methods, tools and systems that can be used for more efficient, secure and reliable operation and management of computer and telecommunication networks. NM Lab is particularly interested in monitoring, analyzing and controlling IP networks. NM Lab's current projects include analyzing captured IP traffic for applications such as network security analysis, workload characterization, performance analysis, billing, SLA management, and customer relationship management.
Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components (defined by NSF, US). Beyond the traditional embedded computing systems that are confined to single devices, CPS can cooperate with various other systems at a large scale. Moreover, CPS are recently becoming smarter by leveraging the rapid advance of machine learning technologies. Several examples of the intelligent CPS include autonomous automotive systems, distributed robot control, monitoring human gestures and behaviors with distributed sensors, etc.
Particularly, we focus on the following topics related to the intelligent CPS.
- Building algorithms that efficiently manage the distributed and restricted resources of CPS
- Creating innovative CPS by leveraging the machine learning techniques
- Addressing real-world problems by our expertise in CPS domain
In Internet of Things Laboratory, A “thing” in the IoT(Internet of Things) can be a person, animal or physical/virtual object with a unique identifier(IP address or device ID) that has the ability to transfer data via the Internet. The “Thing” in IoT usually refers to IoT devices. IoT devices can perform remote sensing, actuating(making an action), and monitoring capabilities. A thing can be smart(cognitive) and thus the thing can make a decision without human’s help(intervention). Majority of things are expected to be smart in the future.
|Chung, In-Jeong||Artifical Intelligencefirstname.lastname@example.org|
|Cho, Choong-Ho||Data Communicationemail@example.com|
|Park, Dai-Hee||Data Miningfirstname.lastname@example.org|
|Chung, Yong-Wha||Parallel Algorithmemail@example.com|
|Kim, Myung-Sup||Network Managementfirstname.lastname@example.org|
|Cho, Hyeon-Joong||Embedded Systemsemail@example.com|
|Jo, Min-Ho||Internet of Thingsfirstname.lastname@example.org|
|Kim, Seung-Yeon||Big data email@example.com|
|Lee, Sung-Ju||Big data firstname.lastname@example.org|