My research interests lie in industrial data science, statistics, artificial intelligence (AI), and nonlinear optimization for energy, manufacturing, and healthcare systems. To date, I have focused on developing digital twin calibration methodologies for energy systems such as building energy and wind power systems. In parallel, I have advanced data science techniques to enhance automation and improve process control in hybrid and smart manufacturing systems. Ongoing research directions include:

• AI-driven digital twins for complex engineering systems
• IoT-enabled data science and AI for improving quality and reliability in large-scale engineering systems

Methodologies

Design and analysis of computer experiments, statistical machine learning, nonlinear optimization, survival analysis, and diffusion model.

Applications

Digital twin calibration, online learning and adaptive control, transfer learning, quality and reliability engineering, and operational decision-making.

Domains

Energy systems (building energy and wind power), manufacturing systems (biomanufacturing and composite materials), and healthcare systems (medical image processing)