25/09/2025
Method that reduces real-world AI training costs and its application to control infectious diseases
To address prevalent data quality issues in real-world AI training, this research focuses on three major challenges. First, it automatically detects and corrects label errors during training, reducing the need for manual data preprocessing. The related paper has had a significant academic impact, being cited over 1,200 times in the past two years. Second, it automatically infers missing labels in time-series data, significantly lowering the cost of manual label acquisition. Third, it removes redundant data and selects a core set of informative samples, achieving comparable model performance while reducing the training time by up to 90%. These technologies have been successfully applied to real-world social problems such as infectious disease prediction and economic impact forecasting. They have been granted patents in both South Korea and the United States.
https://breakthroughs.kaist.ac.kr/sub02/view/page/2/id/6817