Assis Tiago Oliveira Filho
Papers
1
Total Citations
20
H-Index
1
About
Dr. Assis Tiago Oliveira Filho is a leading researcher in the intersection of embedded artificial intelligence and robotics, with a primary focus on enabling deep learning for resource-constrained devices. His most cited work, "FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices" (2022, 20 citations), tackles the critical challenge of deploying computationally intensive neural networks on Internet of Things (IoT) platforms with limited processor power, RAM, and storage. By developing a pruned and quantized convolutional neural network, Dr. Filho has made significant contributions to making robot pose estimation feasible on edge devices, bridging the gap between high-performance AI and real-world hardware limitations. His research directly addresses the pressing need for efficient, on-device inference in autonomous systems, with implications for smart manufacturing, mobile robotics, and ubiquitous computing. Through his innovative approach to model compression and optimization, Dr. Filho is helping to democratize advanced AI capabilities for the next generation of embedded and IoT applications.
Research Focus
Key Achievements
Top Papers
- 1