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연구

연구실소개

Intelligent Enterprise Lab
Intelligent Enterprise LabWebsite
Professor ​Marco Comuzzi Description 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.
Research
Interests		 ​busin​ess process management, enterprise systems, ERP systems, process mining
Data Analytics Lab
Data Analytics LabWebsite
Professor ​Sungil Kim Description 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.
Research
Interests		 Business Analytics, Statistical Quality Control, Anomaly Detection, Data mining and machine learning, Design of experiments, Robust parameter design, Demand forecasting, Predictive analytics
Service Engineering & Knowledge Discovery Lab
Service Engineering & Knowledge Discovery LabWebsite
Professor ​Chiehyeon Lim Description We focus on developing data analytics methods to achieve learning tasks (i.e., knowledge discovery from data), such as representation, generation, prediction, and clustering. Based on such methods, we are also interested in solving real-world service problems with firms and governments (i.e., service engineering with data), including item recommendation, behavioral intervention, process monitoring, and service improvement.
Research
Interests		 Service Engineering and Knowledge Discovery
Data Mining Lab
Data Mining LabWebsite
Professor Junghye Lee Description 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.
Research
Interests		 Data Mining, Probabilistic and Statistical Learning, Machine Learning, Deep Learning, Predictive Analytics, Data Privacy and Security, Health Analytics, Chemometrics
Industrial Intelligence Lab
Industrial Intelligence LabWebsite
Professor Sunghoon Lim Description Our research focuses on developing machine learning and/or social network analysis models for effective knowledge discovery from the real industry. The theoretical components of our research have direct relevance to various areas, including manufacturing (e.g., predictive maintenance, anomaly detection, additive manufacturing), safety management (e.g., car crash detection), and healthcare.
Research
Interests		 Industrial Artificial Intelligence (AI+X), Machine Learning / Deep Learning, Smart Manufacturing/Smart Factory, Social Network Analysis
Financial Engineering Lab
Financial Engineering LabWebsite
Professor Yongjae Lee Description 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 machine learning techniques, and (3) investor data analysis using machine learning techniques. By developing advanced theories and practical technologies, we aim to make it possible for everyone to receive customized life-time financial planning services.
Research
Interests		 Financial Engineering, Financial Technologies(FinTech), Financial Data Analysis, Investment Management
Applied Optimization
Applied OptimizationWebsite
Professor Sangjin Kweon Description 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.
Research
Interests		 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
Learning Intelligent Machine Lab
Learning Intelligent Machine LabWebsite
Professor Sungbin Lim Description 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
Research
Interests		 Stochastic Optimization, Reinforcement Learning, Causal Learning
Statistical Decision Making (SDM) Lab
Statistical Decision Making (SDM) LabWebsite
Professor Gi-soo Kim Description Our research interests are focused on statistical approaches to the sequential decision problem. The multi-armed bandit (MAB) problem formulates the sequential decision problem in which a learner is sequentially faced with a set of available actions, chooses an action, and receives a random reward in response. In our lab, we integrate online learning and optimization techniques to develop algorithms that efficiently learn the reward model while maximizing the rewards. We also apply the developed algorithms to real tasks such as recommendation systems and mobile health apps. We also use causal inference to evaluate the performance of multi-armed bandit algorithms in a retrospective way.
Research
Interests		 Sequential Decision Making, Bandit algorithms, Causal inference, Missing data analysis
Machine Learning and Finance Lab
Machine Learning and Finance LabWebsite
Professor Dong-Young Lim Description The research of Prof. Dong-Young Lim’s lab is focused on stochastic optimization algorithms, nonconvex optimization, and their applications in finance and insurance.
In particular, we are interested in quantitative risk management in financial markets, the development of efficient algorithms for large-scale nonconvex optimization, the study of theoretical properties of such algorithms. Some of our current research projects are

  • · Diffusion-based algorithms for nonconvex optimization and generative model,
  • · MCMC algorithms
  • · AI application in finance and insurance
Research
Interests		 Diffusion-based algorithms for nonconvex optimization. Stochastic optimization algorithm, financial engineering/mathematics