Sung-Su Jang

About

Sung-Su Jang is a researcher at the forefront of artificial intelligence applications in computer vision, with a particular focus on autonomous driving and privacy-preserving technologies. His work bridges the gap between real-time object recognition and ethical AI deployment, addressing critical challenges in both vehicular safety and personal data protection. Jang's most influential contribution, "Driving Scene Understanding Using Hybrid Deep Neural Network" (2019, 9 citations), pioneered a novel hybrid architecture that significantly enhances the ability of autonomous systems to interpret complex driving environments, demonstrating how deep learning can outperform traditional vision methods in real-time scenarios. Building on this foundation, his more recent work "L-GAN: Landmark-based Generative Adversarial Network for Efficient Face De-identification" (2022, 7 citations) introduces an innovative approach to facial anonymization, using landmark detection within GANs to effectively remove identifiable features while preserving natural image quality—a crucial advancement for privacy in surveillance and data sharing. Jang's research not only pushes the boundaries of AI perception but also addresses the growing societal need for responsible AI deployment, making his contributions highly relevant for students and researchers working at the intersection of computer vision, autonomous systems, and ethical technology development.

Research Focus

Key Achievements

Top Papers

1. 1
   Driving Scene Understanding Using Hybrid Deep Neural Network

   9 citations · 2019
2. 2
   L-GAN: landmark-based generative adversarial network for efficient face de-identification

   7 citations · 2022

Key Collaborators

Contact & Links