Scientific, technical, and societal advances are increasingly dependent on new insights, theories, and tools to exploit data effectively for the timely delivery of relevant and accurate information and for knowledge discovery. For the purpose of effective and efficient learning from the data to improve the operational safety, manufacturing efficiency, energy efficiency, and sustainability in manufacturing, our team aims to explore machine learning (ML) and artificial intelligence (AI) for improved information extraction and pattern recognition towards smart, data science-enhanced manufacturing.

Our team targets developing advanced ML and AI techniques suitable for manufacturing data analytics that is featured by high-dimensionality, heterogeneity, non-linearity, and uncertainty. Current research thrusts focus on stochastic processes with distributed filtering techniques, interpretable machine learning, auto-ML, data fusion, orthogonal analysis, and dynamic optimization, with applications on:

• Stochastic modeling for machine performance monitoring, diagnosis, and prognosis, towards predictive maintenance;
• Hierarchical manufacturing process modeling and optimization towards improved manufacturing and energy efficiency;
• Process-structure-property-performance (PSPP) modeling for advanced manufacturing processes, e.g., additive manufacturing;
• Context-aware human action recognition, prediction, robot motion planning for scalable and robust human-robot collaboration;
• Machine-to-machine communication, semantic indexing, processing, and learning for building digitalized manufacturing factories;
• Digital thread in life cycle analysis for improved product design and supply chain management.