معرفی
Feng Dai is affiliated with Xidian University's National Laboratory of Radar Signal Processing in China. His research spans approximation theory, machine learning, computer vision, and optimization algorithms. He has collaborated extensively with institutions like the National Laboratory and co-authored over 140 publications since 2002.
Key research interests include polynomial approximation on multivariate domains, deep learning for computer vision tasks (e.g., object detection, semantic segmentation), and optimization techniques for engineering systems. His work bridges mathematical theory with practical applications in signal processing and imaging systems.
Recent publications focus on advancing polynomial mesh theory, developing algorithms for panoramic imaging, and improving federated learning frameworks for IoT applications. Notable contributions include work on universal discretization methods and boundary handling in oriented object detection.
His interdisciplinary approach integrates computational mathematics with modern AI techniques, addressing challenges in both theoretical and applied domains such as autonomous systems and medical imaging.
