Rashid Abbasi

About

Rashid Abbasi is a leading researcher at the intersection of autonomous driving, 3D computer vision, and next-generation communication systems. His work centers on making smart mobility safer and more efficient through advanced deep learning techniques for LiDAR point cloud processing. Abbasi’s most influential contribution is his comprehensive survey on LiDAR point cloud compression, processing, and learning for autonomous driving—a seminal work that has garnered 87 citations and serves as a foundational reference for researchers tackling the data challenges of self-driving vehicles. He has further advanced the field with his work on 3D LiDAR point cloud segmentation, applying deep learning to improve how autonomous systems perceive and navigate their environments. Beyond autonomous driving, Abbasi addresses critical security and efficiency challenges in 6G Internet-of-Things environments, proposing lossless communication protocols that ensure secure data transmission. His research also extends to surgical robotics, exploring how deep learning can enhance robotic precision in medical applications. With a growing citation impact and a focus on real-world safety and performance, Abbasi is shaping the future of intelligent, autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

   87 citations · 2022
2. 2
   Efficient lossless based secure communication in 6G Internet-of-Things environments

   11 citations · 2023
3. 3
   3D Lidar Point Cloud Segmentation for Automated Driving

   8 citations · 2024
4. 4
   Deep Learning and Robotics, Surgical Robot Applications

   4 citations · 2023

Key Collaborators

Contact & Links