Research Interests
Understanding and optimizing wireless networks is at the core of my research. I explore theory-driven approaches that stay close to real-world implementations, with a particular focus on the intersection between communications and machine learning.
Focus Areas
Foundation Models for Wireless Communications
Building large-scale neural representations of spectrum and channel behavior that transfer across bands, hardware, and deployment scenarios—enabling few-shot adaptation for tasks like signal classification, interference detection, and beam management.Massive MIMO Communications
Designing scalable transceiver architectures that extract the full potential of large antenna arrays under realistic hardware and channel constraints.Integrated Sensing and Communication (ISAC)
Building joint sensing-and-communication frameworks that enable mobile platforms to perceive their environment while maintaining robust connectivity.
Current Directions
- Wireless baseband spectrogram foundation models (LWM-Spectro)
Training generative models on heterogeneous baseband spectrogram captures to uncover reusable neural priors, then adapting them to downstream spectrum sensing and interference diagnostics with minimal labeled data.