Professor (lab) Research Interests Description Homepage
​Marco Comuzzi
(Intelligent Enterprise Lab)

busin​ess process management, enterprise systems,
ERP systems, process mining
This laboratory focuses on engineering of business intelligence tools for enterprise systems implementation and business process management. In enterprise systems, our focus is on the development of models and tools for managing ERP post-implementation changes and understand risk factors in ERP projects. In business process management, our focus is on the application of computational intelligence and data mining techniques to the analysis and optimisation of organisational business processes, using process event logs.
​Changyong Lee
(Business Analytics and Innovation Management Lab)

New business creation based on predictive analytics
Development of methods and an intelligent platform system for business analytics
Artificial intelligence-based prognostics and health management
The Business Analytics and Innovation Management Lab. focuses on the challenges associated with creating new products, services, and businesses and accelerating technology innovation in value-chain processes. We are dedicated to research linking engineering and management disciplines to address the planning, development, and implementation of organisational and technological capabilities, pursuing multidisciplinary approaches and synergetic collaboration with practice.
​Sungil Kim
(Data Analytics Lab)

Business Analytics, Statistical Quality Control, Anomaly Detection, Data mining and machine learning, Design of experiments, Robust parameter design, Demand forecasting, Predictive analytics Dr. Kim's research interests are in the broad areas of data science and business analytics. A major focus of his research is in developing novel statistical methods for solving complex engineering problems. He has several years of consulting experience in solving real business problems in industries.
Chiehyeon Lim
(Service Systems Lab)

Service Science, Data Analytics on Service Systems, Smart Service Systems, Personal Process Management, Decision Science We study engineering and management of service systems, such as transportation, health, home, energy, building, farming, hospitality, education, and game systems. Based on industrial engineering and service science, we analyze both numerical and text data collected from things and people in service systems to understand, evaluate, improve, and design the system mechanisms and operations; we also employ qualitative approach as appropriate. Drawing on real-world lessons from practices and projects, we aim to develop theories and methodologies that advance industrial engineering and service science.
Junghye Lee
(Data Mining Lab)

Data Mining, Probabilistic and Statistical Learning, Machine Learning, Deep Learning, Predictive Analytics, Data Privacy and Security, Health Analytics, Chemometrics We are pursuing to develop best algorithms, systems, and applications especially for predictive analysis and data privacy and security, which help solving important industrial and management problems and creating value.
Sunghoon Lim
(Unstructured Data Mining and Machine Learning Lab)

Unstructured Data Analytics, Machine Learning, Artificial Intelligence, Natural Language Processing/Text Mining Our research focuses on developing machine learning models for effective knowledge discovery from unstructured data. The theoretical components of our research have direct relevance to various areas,​ including customer feedback analysis, healthcare (e.g., new disease detection), finance (e.g., stock market prediction), and anomaly detection in manufacturing.
Yongjae Lee
(Financial Engineering Lab)

Financial Engineering, Financial Technologies (FinTech), Optimization, Investment Management, Financial Planning We study quantitative approaches to financial planning of individuals and institutions. Most research topics can be categorized into three: (1) making optimal investment decisions using optimization and machine learning, (2) financial market modeling using econometrics and pattern recognition, and (3) investor data analysis using data science techniques. By developing advanced theories and practical technologies, we aim to make it possible for everyone to receive customized life-time financial planning services.
Sang Jin Kweon
(Applied Optimization)

Optimization problems in the sharing economy, logistics and transportation sectors, and their effects on energy sustainability and environment
Development of polynomial-time algorithms for solving optimization and network problems
The mission of the Applied Optimization Lab is to conduct high quality academic research while addressing real industrial and government problems. Research activities are focused on the use and development of advanced computer software to analyze and optimize performance measures of actual systems. The lab’s faculty participants have a unique combination of expertise and experiences that allow them to address complex problems in logistics, transportation, and renewable energy systems, stochastic modeling and analysis of manufacturing systems, facility layout and location, and network design and optimization.