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.
​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 Engineering & Knowledge Discovery Lab)

Service Engineering and Knowledge Discovery We solve service problems in industries and develop knowledge discovery methods. Specifically, the service engineering topics include service intelligence development and service quality evaluation and improvement, while the knowledge discovery topics include text mining, behavioral data mining, and clustering method development.
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)

Machine Learning / Deep Learning, (Unstructured) Data Mining, Industrial Artificial Intelligence (AI+X), Social Network Analysis, Crowdsourcing Our research focuses on developing machine learning and/or social network analysis models for effective knowledge discovery from unstructured data. The theoretical components of our research have direct relevance to various areas,​ including safety management (e.g., car crash detection), manufacturing (e.g., predictive maintenance, anomaly detection, additive manufacturing), customer feedback analysis (e.g., social media, online customer reviews, recommender systems), and healthcare.
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.
Sangjin 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.
Sungbin Lim
(Learning Intelligent Machine Lab)

Stochastic Optimization, Reinforcement Learning, Causal Learning Learning Intelligent Machine Lab focuses on Artificial Intelligence (AI) and its applications to industrial and scientific problems. Specifically, we pursue principled approaches to incomplete data problems in machine learning, including deep learning, via the view of statistics and mathematics. These topics are profoundly related to statistical learning, meta learning, and causal reasoning. Currently, I am working with the following research topics:
• Causal Learning / Machine Reasoning
• Statistical Learning
• Uncertainty Estimation
• Stochastic Optimization
• Planning / Reinforcement Learning
• Automated Machine Learning