About Lab

Our research interest lies in the intersection of
data mining, machine learning, and optimization.

In the perspective of developing methodologies,
it focuses on investigating the source of possible leverage in improving data mining and machine learning methods from real-world applications.
Applying machine learning and optimization techniques to the real-world problems is another direction of our research.

Since, most of the times, the design of new algorithms is required by a specific application, and doing so provides strategic advantages,
we have been doing our research to perform the former and latter simultaneously.

We also have an interest in applying data-driven models to system control for implementing manufacturing intelligence and other system intelligence.
Working with various types and large amount of data from real fields,
the overall goal of our research is to help organizations make better decisions based on scientific evidences and to create intelligence systems.

Research Areas


  • Imbalanced Data Classification and Regression
  • Advanced Techniques in Unsupervised Learning
  • Anomaly Detection
  • Ensemble Learning and Pruning
  • Transfer Learning / Few Shot Learning / Deep Learning General
  • Data Stream Clustering
  • Object Counting


01Manufacturing Intelligence for Smart Factory

  • Artificial Intelligence for Manufacturing
  • Machine Learning-Based Function Estimation and Automatic Control
  • Real-Time Optimization for Equipment Control

02Service Intelligence

  • Price Forecasting for Fleet Management
  • Financial Fraud Detection
  • Recommender Systems