C.‐C. Jay Kuo

About

C.-C. Jay Kuo is a pioneering researcher whose work spans computer vision, robotics, and multimedia signal processing. His most influential contributions lie in scene recognition for mobile robots, where he pioneered the use of audio features to classify unstructured environments—a complementary approach to traditional visual methods. His seminal 2006 paper, "Where am I? Scene Recognition for Mobile Robots using Audio Features," has garnered over 150 citations, establishing a foundation for auditory scene analysis in robotics. Kuo also made significant advances in 3D point cloud analysis, efficient VLSI design for SIFT feature description, and rotation-invariant shape retrieval for medical databases. Notably, his work on adversarial human-robot learning introduced novel frameworks for robots to learn from non-cooperative human supervisors, challenging conventional human-in-the-loop paradigms. With over 200 citations across his most-cited works, Kuo’s research continues to influence autonomous systems, medical imaging, and efficient hardware implementations, making him a key figure in bridging perception, learning, and real-world robotic applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Where am I? Scene Recognition for Mobile Robots using Audio Features

   151 citations · 2006
2. 2
   3D Point Cloud Analysis

   29 citations · 2021
3. 3
   Content Analysis for Acoustic Environment Classification in Mobile Robots.

   14 citations · 2006
4. 4
   Efficient VLSI design for SIFT feature description

   11 citations · 2010
5. 5
   Efficient Rotation Invariant Retrieval of Shapes with Applications in Medical Databases

   5 citations · 2006
6. 6
   Robot Learning via Human Adversarial Games

   4 citations · 2019
7. 7
   Robot Learning via Human Adversarial Games

   3 citations · 2019

Key Collaborators

Contact & Links