Data-Driven Methods for Sustainable Industrial and AIoT Systems

Last updated on Jun 12, 2025
Generated by Microsoft Designer

Conventional industrial systems and emerging systems such as data centers, 5G communication networks consume enormous amount of energy and non-renewable resources. We focus on developing advanced data-driven AI methods to optimize real-world complex industrial and AIoT systems. Helping the related industries to improve operation efficiency, save energy, reduce emission, and ultimately achieving the goal of green and sustainable development.

Our current research focus includes:

Simulator-Free Optimization

Data-driven control optimization for complex industrial systems

Data Center Efficiency

Energy saving optimization for data centers

5G Beamforming

Massive MIMO Beamforming optimization for 5G

Hybrid RL

Engineering policy integrated hybrid reinforcement learning

Latest Achievement

Data Center Cooling System Optimization

AIoT
Xianyuan Zhan
Xianyuan Zhan
Faculty Member

Publications

Data Center Cooling System Optimization Using Offline Reinforcement Learning
The recent advances in information technology and artificial intelligence have fueled a rapid expansion of the data center (DC) …
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Data Center Cooling System Optimization Using Offline Reinforcement Learning
TESLA: Thermally Safe, Load-Aware, and Energy-Efficient Cooling Control System for Data Centers
The increasing demand for artificial intelligence and cloud computing has led to skyrocketing energy consumption of data centers (DCs). …
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A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement …
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Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets …
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An Efficient Multi-Agent Optimization Approach for Coordinated Massive MIMO Beamforming
Beamforming plays an important role in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications. Optimizing beamforming …
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DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy …
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