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.