Ramyad Hadidi

Papers

Total Citations

210

H-Index

About

Ramyad Hadidi is a computer systems researcher specializing in edge computing, deep neural network (DNN) deployment, and collaborative robotics. His work sits at a critical intersection of machine learning and systems engineering, addressing one of the field's most pressing challenges: enabling resource-intensive AI models to run efficiently on constrained edge devices. Hadidi's most influential contribution, "Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices" (2019, 109 citations), provided the research community with a rigorous empirical foundation for understanding the performance limitations of DNN inference at the edge. Complementing this, his highly cited work "Distributed Perception by Collaborative Robots" (2018, 88 citations) demonstrated how networks of robots could share computational burdens to achieve real-time perception — a breakthrough for practical multi-robot systems operating under tight resource constraints. His research consistently tackles the fundamental tension between DNN computational demands and the limited processing power of edge hardware. His later work on low-communication parallelization methods further advances practical deployment strategies for IoT and autonomous agents. Through rigorous benchmarking and novel system design, Hadidi has made meaningful contributions that guide both academic researchers and industry practitioners working to bring intelligent perception to the edge.