My research focuses on applying machine learning to enhance IoT and low-power wireless networks under coexistence and adversarial conditions. I build telemetry-driven learning and control loops that infer channel and interference conditions and improve protocol and signal processing decisions, validated through controlled SDR testbeds and reproducible evaluation.

• Cross-technology communication: signal-level interoperability between LR-FHSS and LoRa, including receiver-side detection and correction.
• Learning-based coexistence: PyTorch-based adaptation and coordination for dense multi-network environments using telemetry and feedback.
• Jamming defense: gateway-side recovery and multi-gateway diversity against reactive and collaborative jammers.
• LPWAN MAC/PHY design: measurement-driven mechanisms with predictable reliability and latency under realistic constraints.