Welcome to Autonomous Edge Intelligence Lab (AEI Lab)

This research explores multi-agent and hierarchical reinforcement learning for complex sequential problems in dynamic environments. Key topics include temporal abstraction, distributed coordination, and robust policy learning, with particular emphasis on enabling autonomous agents to cooperate effectively under uncertainty and system limitations. 

Research in this area focuses on intelligent computing and collaborative learning across the device-edge-cloud continuum. Core topics include federated and distributed learning, task offloading, caching, resource allocation, edge AI, and collaborative inference. The goal is to support efficient and adaptive orchestration across heterogeneous and resource-constrained systems. 

This pillar addresses intelligent networking, adaptive connectivity, and coordinated autonomy in connected mobility systems. Research topics include autonomous navigation, trajectory planning, mobility-aware control, and cooperative sensing, communication, and coordination across heterogeneous platforms, with applications in IoV, VANETs, V2X, UAVs, FANETs, and connected vehicles. 

The focus of this pillar is learning-based control and optimization for intelligent cyber-physical systems and smart infrastructure. Current interests include adaptive traffic control, intelligent transportation, EV charging, and energy-aware coordination, with applications in smart cities, transportation networks, EV charging infrastructure, and energy systems.