+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
High Technolgy Manufacturing & Automation, Service System
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.
+82-42-350-3157 IE B/D E2-2 NO.3113 http://fineconomics.kaist.ac.kr/새창
Financial institutions, Financial products, Financial regulations
The goal of Financial Economics Lab is to understand behaviors and regulations of financial institutions in the shadow banking system and the role of structured financial products in the shadow financial intermediation process. What happened during the subprime mortgage financial crisis of 2008-2009? How did the securitization products including subprime mortgage backed securities (MBSs) and collateralized debt obligations (CDOs) contribute to the financial crisis? Our lab studies the global financial system, financial products, and financial institutions in the perspective of the current event including the European sovereign debt crisis.
Our Research Areas
• Financial institutions in the Shadow banking system such as investment banks, hedge funds, and money
market funds (MMFs)
• Financial products including securitization products (MBSs, CDOs, and ABCP), repurchase agreements
(Repo), and derivatives
• Financial regulations
• The subprime financial crisis of 2008-2009
• The European sovereign debt crisis
+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.
+82-42-350-3160 IE B/D E2-2 NO.4115, 4116 http://fms.kaist.ac.kr새창
제조 시스템 설계 및 운용, 공급 사슬(Supply chain) 전략체계 수립 및 운용, 설비 투자 및 배치 계획, 프로젝트 스케쥴링, 물류 수송 계획
FMS/PM 연구실에서는 반도체, PCB, LCD, PDP 등의 첨단 제품들(High-tech products)에 대한 생산 시스템의 설계 및 운용 합리화를 위한 연구를 수행하고 있습니다. 운용 합리화란 시스템의 효율이나 고객 납기 준수율의 향상과 같은 목표를 달성하기 위하여 생산에 관련된 모든 프로세스를 최적화하는 활동을 뜻합니다. 이를 이루기 위해 생산계획 및 일정계획, 공급사슬관리에 관한 연구를 진행하고 있습니다.
생산계획 및 일정계획에 대해서는 현재까지 흐름공정, 조립라인, RMS(reconfigurable manufacturing system)과 같은 다양한 생산 환경을 대상으로 하여 연구를 수행해 왔고, 보다 실효성 있는 연구를 위해 생산 환경내의 다양한 제약사항들을 고려한 연구를 지속해서 진행하고 있습니다. 그리고 이러한 연구가 실제로 생산 시스템에 구현되어 운용 합리화를 달성할 수 있도록, 다양한 소프트웨어 패키지를 경영과학 이론 및 최적화 방법론을 응용하여 개발해 왔습니다.
공급사슬관리(SCM; supply chain management)란 상품의 공급과정을 시장상황에 최적화되도록 함으로써 경영효율을 높이는 활동을 뜻합니다. 이를 위해 재고계획 및 운송 계획에 대한 다양한 연구가 이루어져 왔으며, 뿐만 아니라 유비쿼터스(Ubiquitous) 기술을 기반으로 하는 VMI (vendor managed inventory) 시스템의 운용 합리화에 대한 연구를 활발히 진행하고 있습니다.
FMS/PM 연구실은 1990년도에 설립되어 약 27년간 23명의 박사학위 수여자와 49명의 석사학위 수여자를 배출하였으며, 이들은 학계와 제조 및 금융업계, 컨설팅업계 등의 다양한 분야에서 활발히 활동하고 있습니다. 현재 연구실 구성원은 교수님 및 박사과정 5명, 석사과정 4명으로 구성되어 있습니다.
+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 http://ic.kaist.ac.kr/새창
Social Q&A and Beyond, Mobile (Social) Computing Systems, Networked Collaboration Platforms
Recent advances in computing technologies, such as smartphones, Web 2.0 services, Internet of Things (IoT), and wearable devices, have substantially facilitated networked collaboration among people (e.g., social networks, Wikipedia, social Q&A) and also enabled various ubiquitous computing services (such as location-based services and smart home services).
KAIST Interactive Computing Lab’s major research area has been designing, evaluating, and understanding ubiquitous computing and social computing systems that are often situated in real social contexts and are made to appear anytime and everywhere. In particular, our research focus 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).
Our research methods include analyzing multiple data sources ranging from sensor data to interaction data to understand user needs and find systems design implications, and building/deploying novel ubiquitous and social computing systems. The data collection step involves sensor data acquisition from wireless sensors and wearable devices; the data analysis step requires statistical machine learning and data mining techniques (to uncover generic patterns from data; and to automatically detect events and objects of interests); and the artifact building step aims to iteratively implement a working prototype and to evaluate its performance and user experiences in the wild.
+82-42-350-1614 IE B/D E2-1 NO.1205 http://knowlab.kaist.ac.kr/새창
Knowledge Modeling and Web Services: Mapping Context and Ontology, Knowledge Mapping, Knowledge Ext
Can we model all the knowledge in the world? Does all the knowledge have the same basic structure? Alternatively, can we find a knowledge processing mechanism which is platform independent? Know Lab aims to find solutions to these questions. We look at knowledge processed at a high level in areas such as research articles, patents, medicine, chemistry, technology, and Web based platforms. We try to identify “acceptable behavior” of knowledge and the rules that govern this knowledge activity. All in favor of brain detachment are welcome.
+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.
+82-42-350-3168 IE B/D E2-2 NO.2111 http://risklab.kaist.ac.kr새창
Simulation methods for stochastic models; Risk management in finance and operationsRisk Lab. aims to develop methods for the better understanding of internalized risks within products and systems. Our approaches are based on quantitative analysis, using various techniques from probability, stochastic processes, simulation, and optimization. Our recent interests include systemic risk measurement of financial networks, pricing and hedging of financial products, analysis and management of energy disruption risks, spatial extreme risk, and implied volatility surfaces.
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.).
Cellular Communication, Wireless Internet, Genetic Algorithms/Tabu Search
-Genetic Algorithms/Tabu Search