+82-42-350-3158 IE B/D E2-2 NO.4102 http://aai.kaist.ac.kr/새창
A.l. Intelligence; M&S for Emerging smart markets, Stock market, Military&intelligence organizationsAAILab researches on how to understand, design, and manage complex socio-economic systems in our societies. Diverse complex socio-economic systems exist in our societies, and some of them are listed in the below.
+82-42-350-3123 IE B/D E2-2 NO.4106 http://cypress.kaist.ac.kr/새창
Product Development Strategy, Semantic PLM, Ontology Engineering
Complex System Design, Healthcare Delivery SystemData Mining Lab was initiated in December 2010 when Prof. Lee joined KAIST. Our lab is investigating various issues and challenges of data mining which is roughly defined as extraction of interesting (non-trivial, implicit, previously unknown, and potentially useful) patterns or knowledge from huge amount of data. We are developing innovative data mining algorithms and creative knowledge services. We have done world-class research published at premier venues such as PVLDB, SIGMOD, KDD, ICDE, TKDE, and VLDB J.
+82-42-350-3169 IE B/D E2-2 NO.3117 http://felab.kaist.ac.kr/새창
Financial Optimization, Portfolio Theory, Investment Management, Financial Markets
Financial Engineering Laboratory is a research group in the department of Industrial and Systems Engineering at KAIST.
We are a member of KAIST Financial Engineering Group.
+82-42-350-3172 IE B/D E2-1 NO.4220 http://hfel.kaist.ac.kr/새창
Human factors/ergonomics, HMIHuman Factors and Ergonomics Lab (HFEL) at KAIST aims to develop novel human factors and ergonomics methodologies, techniques and models (especially digital human modeling, biomechanics and ICT based) to understand human capabilities, limitations, and interactions among humans and other elements of a system. By applying theory, principles, data and methods from science and engineering, our lab designs human-centered products, tasks, environments and systems for improving safety, health, comfort and human performance.
+82-42-350-3165 IE B/D E2-2 NO.3115 http://istat.kaist.ac.kr/새창
Data mining and machine learning Nonparametric and semiparametric statistical methods Functional datiStat Lab. (Industrial Statistics Labatory) is a laboratory in the department of Industrial and Systems Engineering at KAIST (Korea Advanced Institute of Science and Technology). Major reserach interests include applied statistics and data mining. Academic advisor is Prof. Heeyoung Kim.
+82-42-350-3159 IE B/D E2-2 NO.3125 http://isl.kaist.ac.kr/새창
Cognitive System Engineering; Human-System Safety; HCI (Human-Computer Interaction) & UI (User Inter
We aim to construct a bridge that connects, or combines, the human resources and the computer power and to enhance the interaction between the two. This mission calls for the knowledge and a good practice of HCI(Human Computer Interaction), Cognitive Systems Engineering, and Interactive Optimization techniques.
The main discipline of our research is referred to as Cognitive Systems Engineering(CSE). CSE is concerned with improving the performance of information processing activities in systems that include both humans and computers. CSE research requires a global view and integrative methods embracing Cognitive Psychology, Cognitive Science, Artificial Intelligence, and Systems Engineering as the diagram indicates. A goal-oriented attitude and engineering spirit are also required to make practical applications succeed.
+82-42-350-1616 IE B/D E2-1 NO.1206 https://ic.kaist.ac.kr새창
Human-Computer Interaction, Ubiquitous Computing, Social Computing, Data ScienceOur group’s research fields include (1) Human-Computer Interaction, (2) Ubiquitous Computing, (3) Social Computing, and (4) Data Science and Internet of Things (IoT). Our research goal has been on the design of socially empowered, intelligent knowledge service systems that promote knowledge sharing, decision making, and wellbeing. Furthermore, we have been building enabling technologies that are essential for providing intelligent knowledge services (e.g., vibration sensing, location sensing, activity recognition, wireless networking).
+82 42 350 1602 IE B/D E2-1 NO.1201 http://kirc.kaist.ac.kr/새창
Experiential Knowledge Acquisition & Utilization; Big-data Analytics; Personalized ServicesWe are mostly interested in innovating user centered knowledge Services that support human intellectual activities.
Decision making, system thinking and decision support; Machine learning and pattern recognition; Mon
Interests of the laboratory cover the area of system analysis ranging from mathematical algorithms of optimization, dynamic programming and optimal control to the practical probabilistic modelling and further decision making applications.
The fields of the applications are broadly ranging from sports to financial engineering. We also have a long tradition in CAD/CAM, but nowadays it’s not main focus of laboratory.
+82-42-350-3170 IE B/D E2-2 NO.4117 http://sdm.kaist.ac.kr/새창
Systems optimization of wireless power electric transportation, System design and analysis of automa
Operational Optimization for Semiconductor Manufacturing Tools, Discrete Event System Scheduling, De
Real-time data collection/processing framework, Real-time adaptive learning algorithms for distribut
System Intelligence Laboratory seeks to develop data-driven approaches to analyzing, monitoring, and controlling complex engineering systems, with an attempt to optimize designs, managements, and operations of various target systems.
Systems Intelligence Laboratory seeks to realize systems intelligence that allows automated systems analysis and decision makings to improve system performances and reliability. The core elements for realizing systems include (1) sensing system, (2) data analytics, and (3) decision-making. We conduct research on each of these as well as try integrate them systemically to link raw sensor data to the optimum decision regarding the target system, thus closing a feedback loop.
Systems Intelligence Laboratory tries to solve various systems problem employing data-driven modeling and control approaches. As systems become larger and more complicated, it is becoming harder to derive analytical models describing the behaviors of the target systems. For such case, data-driven approaches can be a good alternative to solve challenging problems. The main target systems include energy systems (i.e., wind turbine, wind farm, nuclear, etc.), manufacturing systems, and urban infrastructure systems (traffic network, smart grid, etc.).
042-350-3134 IE B/D E2-1, NO. 4207 https://msslab.kaist.ac.kr/새창
Research AreaSystems Modeling, Scheduling, and ControlReinforcement Learning for SchedulingSmart Factory with Machine LearningProduction & Logistics ManagementDesign and Operations of Warehouse Automation Systems
042-350-3138 IE B/D E2-1 No.4210 https://plhlee2010.wixsite.com/work새창
Immersive Technologies, Interactive Computing, HCIOur current research interests are Extended Reality (XR, AR, VR, MR), Human-Computer Interaction (HCI), Human-Drone Interaction, and Edge AI for Interactive XR systems.
042-350-3137 IE B/D E2-2 NO.4104 http://dsail.kaist.ac.kr/새창
Artificial Intelligence, Data MiningWe are Data Science & Artificial Intelligence Lab (DSAIL) at KAIST led by Prof. Chanyoung Park. Due to the recent expansion of social media and online communities, online platforms in the digital economy are inundated with vast amounts of usergenerated multimodal (heterogeneous) data from various sources, which can be categorized into structured (e.g., graphs such as social network) and unstructured data (e.g., text, image, video, and audio). When properly analyzed, such multimodal data can be a valuable asset to the companies, but it is challenging not only due to the difficulty in extracting meaningful information from the inherently sparse and noisy data, but also in combining and customizing the extracted knowledge from different modalities with different statistical properties to facilitate various target applications.
042-350-3140 #2118, E2-2 https://highstat.kaist.ac.kr/home새창
Statistics, Data Science, Statistical Machine LearningIn our lab, we develop statistical methodologies for high-dimensional, complex problems that arise in various scientific fields. We especially focus on enhanced interpretability of our statistical solutions.