About me

I joined Carnegie Mellon University as a Ph.D. student in 2019 after graduating with highest honors in Physics and Computer Science from the University of North Carolina at Chapel Hill. I am currently pursuing my Ph.D. in the Department of Electrical and Computer Engineering (ECE), co-advised by Professors Bob Iannucci and Carlee Joe-Wong.

My PhD research focuses on optimizing decentralized learning applications for resource-constrained edge and IoT systems. The central theme of my work is developing both device-level and system-level strategies to efficiently distribute learning tasks across networks with heterogeneous and correlated client data and resources. This involves reducing computation and communication loads at both the device and network levels, leveraging techniques such as reinforcement learning, graph neural networks, multi-armed bandits, and quantization.

I am broadly interested in the challenges of distributed and networked systems and how they intersect with emerging technologies. My interdisciplinary background allows me to approach problems from multiple perspectives, and I am driven by the potential to bridge the gap between theory and practical engineering solutions.

Research Topics

• ML-enabled systems optimization (applied RL, GNNs, bandits)
• Low-power/edge computing (e.g., quantization)
• Decentralized learning (e.g., FL, correlated data analytics)