Key research areas
Materials synthesis and processing
Full process integration
Materials Synthesis and Processing focuses on revolutionizing traditional manufacturing by integrating Artificial Intelligence (AI) into every stage of production—from initial material discovery to final product design.
Material compositions
By leveraging expertise in diverse materials such as metals, ceramics, plastics, and advanced functional materials (e.g., carbon, MXenes), the team aims to optimize material compositions and equipment configurations to maximize productivity and quality while minimizing costs.
Leveraging machine learning
The core of this research involves replacing trial-and-error methods with data-driven advancements, utilizing machine learning, predictive modeling, and deep learning to accelerate the development of next-generation materials and detect defects in real-time.
Automation and robotics
AI integration with robotics
Automation and Robotics focuses on integrating advanced artificial intelligence and machine learning to elevate robotic autonomy, adaptability, and decision-making within complex industrial environments. By leveraging AI-driven perception, multi-modal sensor fusion, and sophisticated computer vision, this research aims to develop self-learning systems capable of real-time object recognition and precise control.
Collaborative robotics
Utilizing cutting-edge techniques such as reinforcement learning and imitation learning, the team works to optimize robot path planning and predictive maintenance, ultimately enabling collaborative robotics where machines learn from sensor feedback to work safely and efficiently alongside human operators.
Robotics in production and assembly
This research area transitions from theoretical AI to practical, high-impact applications through state-of-the-art R&D facilities. Key projects include the development of AI-driven automated assembly and quality control for large-scale energy storage systems, as well as adaptive robotic grasping in cluttered manufacturing settings. By combining robotics expertise with AI experimentation, the research seeks to solve modern manufacturing challenges, significantly increasing productivity, precision, and operational reliability in the era of smart automation.
System monitoring and control
Sensor integration and data acquisition
System Monitoring and Control focuses on developing intelligent frameworks for the continuous oversight and real-time management of modern manufacturing systems. By integrating IoT devices, sensors, and data acquisition pipelines, this research leverages artificial intelligence to transform raw operational data into actionable insights.
ML-enhanced decision support systems
These capabilities enable precise anomaly detection, failure prediction, and automated decision-making, ensuring that manufacturing processes maintain high standards of quality, safety, and reliability while maximizing overall equipment effectiveness.
Realized process efficiency of large-scale industrial operations
Through advanced AI models—including reinforcement learning and predictive analytics—this research designs dynamic scheduling for flexible job shops, adaptive control for flow shops, and integrated predictive maintenance models. These innovations are engineered to enhance process efficiency and robustness of large-scale industrial operations, ensuring they remain resilient in the face of operational uncertainties.
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