Research
My research develops learning and signal-processing methods for wireless and networked systems across three connected areas: generalizable wireless models, network intelligence, and semantic communications for AI systems.
Foundation and Generative AI for Wireless Systems
I develop foundation and generative models that learn reusable representations of wireless signals and environments. Building on earlier work in channel inference and end-to-end communication, this direction seeks wireless models that generalize across tasks and operating conditions.
Network Intelligence and Edge AI
I develop learning methods for resource allocation, coordination, and adaptation in wireless networks. The work spans reinforcement learning, graph neural networks, and distributed learning, with the goal of enabling network intelligence across changing devices, topologies, and operating conditions.
Semantic Communications for AI Systems
I study semantic communication systems that prioritize task-relevant information for intelligent applications. Current work focuses on cooperative perception and reliable semantic transmission, with future directions in multimodal models and communication among AI systems.