S. Faegheh Yeganli
Papers
1
Total Citations
4
H-Index
1
About
S. Faegheh Yeganli is a researcher at the forefront of efficient deep learning and collaborative intelligence, with a primary focus on optimizing Deep Neural Networks (DNNs) for resource-constrained environments like the Internet of Things (IoT). Her most cited work, "DSPCI-MTL: Dynamic split point computing in multi-task learning implementation with collaborative intelligence" (2025, 4 citations), introduces a novel framework that dynamically partitions computational tasks between edge devices and cloud servers. This breakthrough enables multi-task robots and swarm systems to process complex image feature maps more efficiently, balancing accuracy with latency and energy constraints. Yeganli’s contributions are pivotal for advancing real-time AI in autonomous systems, where collaborative intelligence is essential. Her research addresses a critical bottleneck in deploying DNNs in IoT settings, making her work highly relevant for students and engineers exploring edge computing, multi-task learning, and distributed AI. With a growing citation footprint, Yeganli is establishing herself as a key voice in the intersection of deep learning and practical, scalable deployment.
Research Focus
Key Achievements
Top Papers
- 1