Professional Specialty：

Dedicated to the research and development of Industrial Artificial Intelligence (Industrial AI), smart manufacturing, and advanced process control technologies, with the goal of helping industries adopt AI-driven intelligent decision-making, process optimization, and digital transformation.

• Smart Manufacturing and Industrial AI: Applying artificial intelligence, big data analytics, and machine learning to smart manufacturing and process optimization.
• AI Predictive Modeling and Intelligent Process Control: Developing high-accuracy predictive models integrated with advanced control strategies to enhance process stability, operational efficiency, and economic performance.
• Smart Sensing, Virtual Metrology, and Fault Diagnosis: Leveraging neural networks, deep learning, and machine learning techniques to achieve real-time quality prediction, equipment health monitoring, and anomaly detection.
• Generative AI and Industrial Data Intelligence: Utilizing generative artificial intelligence for data augmentation, knowledge extraction, and AI-assisted decision support to accelerate industrial intelligence and digital transformation.

Research Fields（Modeling）：

• Next-Generation Multi-Resolution Latent Variable Learning: Developing high-performance analytical models for smart manufacturing to enhance process monitoring, diagnosis, and intelligent decision-making.
• Tensor Learning and Foundation Models for High-Dimensional Process Intelligence: Advancing large-scale AI models for complex industrial processes to improve data analytics, representation learning, and knowledge extraction.
• Attention-Based Deep Learning: Developing intelligent process systems with autonomous perception, reasoning, and decision-making capabilities.
• Reinforcement Learning for Data Reconciliation and Fault Diagnosis: Integrating AI-driven autonomous learning with data reconciliation and fault diagnosis to enhance process reliability and improve anomaly detection accuracy.
• Physics-Informed and Explainable AI (XAI): Developing intelligent process models that integrate physical knowledge with explainable AI to achieve high accuracy, interpretability, and trustworthy decision-making.

Research Fields（Control）：

• Safe Autonomous Process Control (Control-Aware Reinforcement Learning): Developing intelligent control technologies that jointly optimize safety, autonomy, and control performance for advanced industrial processes.
• Integration of Large Language Models (LLMs) and Machine Learning: Developing AI-assisted systems for process design, intelligent control, and decision support to improve engineering productivity and decision quality.
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Selected Publications：

1. Zhu, X. Wang, Z. Li, and J. Chen, Enhancing Quality Prediction in Industrial Processes with Spatiotemporal Attention-Based Data Techniques, IEEE Trans. Autom. Sci. Eng., 23, 9107-9118, 2026.
2. -S. Lee and J. Chen, Enhancing Within-Batch Quality Prediction by Cyber-Physical Latent State Models and Incomplete First-Principles, Eng. Appl. Artif., 174, 113982, 2026.
3. Liu, P.-H. Chen and J. Chen, Advanced Batch Monitoring: Auto-Segmentation of Complex Processes Using Slow Feature Partial Least Squares, Adv. Eng. Inform., 69, Part B, 103895, 2026.
4. Ren, Y. Shi, F. Sun, W. Zhang and J. Chen, Enhanced Probabilistic Forecasting of Wind Power Using a Distribution-Free PDF Evolution Model, Renewable Energy, 258, 124973, 2026.
5. -S. Lee, S.-K. Ooi and J. Chen, Developing Multi-step Ahead Quality Prediction Models for Early Warning Computers & Industrial Engineering, Comput. Ind. Eng., 210, 111555, 2025.
6. Rochpuang and J. Chen, Optimizing Organic Rankine Cycle Systems: A Hierarchical Reinforcement Learning Approach to Design and Control, Appl. Thrm. Eng., 278, D, 127337, 2025.
7. -S. Lee and J. Chen, A Novel Reinforced Incomplete Cyber-Physics Ensemble with Error Compensation Learning for Within-Batch Quality Prediction, Adv. Eng. Inform., 65, 103172, 2025.
8. Zhu, J. Chen and C.-I Chen, Prognostics for Semiconductor Sustainability: Tool Failure Behavior Prediction in Fabrication Processes, IEEE Trans. Syst. Man Cybern., 54(6), 3450-3460, 2024.