I am a first-year PhD student in Computer and Information Sciences at Towson University, advised by Prof. Yifan Guo. My research is primarily in distributed machine learning, specifically making federated learning efficient for resource constrained edge devices.

During my Master’s degree, I worked on Edge AI systems, studying energy, power, and performance trade offs on real hardware platforms. I explored the deployment of quantized large language models on edge devices using instrumented Raspberry Pi testbeds with thermal and power monitoring.

My current research extends this line of work from single device optimization to distributed federated learning settings. In particular, I focus on model compression and encoding techniques for heterogeneous edge devices in federated learning, aiming to reduce computation and communication overhead while preserving performance.

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Publications

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[Paper] [Demo]