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Research Goal
Research Goal
Conducting research into AI-driven problem-solving for large-scale infrastructures, such as plant systems, cities, factories, buildings, farms, and diverse facilities.
Conducting research on Energy-AI encompassing Demand Response, Optimization, Energy Trading, Smart Grid, Distributed Energy Resources, Energy Storage Systems, Building Energy Management System, and Renewable Energy Systems.
Employing statistical methods within the AI-Industry to extract valuable insights from Big Data.
Developing machine learning algorithms customized for practical applications across different domains.
Conducting research on Industrial AI that fuses physics‑informed learning, digital twins, and edge intelligence to enable robust, real‑time decision‑making and control for safety‑critical systems.
Advancing LLM/VLM methods tailored to industrial contexts, focusing on robust deployment, domain adaptation, and practical impact.
Applications(including but not limited to)
Applications(including but not limited to)
Energy-AI
AI-based Energy big data, Demand Response, Optimization, Energy trading, Smartgrid, Distributed Energy Resources, Energy Storage Systems, Renewable energy systems
Building Energy & Control - Building energy, HVAC, Optimization
Battery Performance Analysis
Industrial AI
Fault Diagnosis & Anomaly Detection - Prognostics and health management (PHM), Anomaly detection, Estimation, Optimization
AI based Risk Analysis - Disaster, Hazard estimation, Software development for loss estimation
Data Science & Open Data based Analysis for Practical domain
AI & Machine Learning in Industrial Applications - Deep learning, Reinforcement learning, Swarm intelligence for New Industry
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