Dr Shuang Dai

Shuang is a Postdoctoral Research Associate at the University of Exeter. Her research integrates cutting-edge AI technologies to enhance efficiency and sustainability in engineering and energy systems, with a focus on real-time data processing, predictive modeling, and privacy-preserving AI. Her expertise spans data mining, time-series forecasting, and distributed learning, with a particular emphasis on applying Industry 4.0 technologies to optimize smart infrastructure, industrial automation, and energy systems. Shuang completed her PhD in Data Science at University of Essex, in 2023. 

 

• Time-Series Forecasting and Predictive Analytics: Developing AI-driven models for energy demand prediction and load profiling in smart grids and industrial systems.
• Federated and Distributed Learning for Smart Infrastructure: Designing privacy-aware, decentralized AI frameworks for energy analytics, smart metering, and industrial automation.
• Spatiotemporal Data Mining and Anomaly Detection: Applying functional data analysis and deep learning to analyze and optimize building energy efficiency and smart grid performance.
• Multimodal and AI-driven Decision Support Systems: Integrating modality-aware fusion networks and optimization algorithms to enhance risk assessment, adaptive control, and decision-making in Industry 4.0 environments.

 

• Smart Energy Systems and Predictive Load Forecasting: Real-time energy forecasting, peak load management, and demand-side optimization. Disaggregated energy monitoring, load identification, and user behavior analysis.
• Smart Buildings and Infrastructure Optimization: Energy management systems, predictive maintenance models, and dynamic thermal control strategies to improve building sustainability.
• Industrial AI and Engineering System Intelligence: Focusing on improve manufacturing efficiency, predictive maintenance, and industrial energy consumption.