Chang‐Sung Jeong

Papers

Total Citations

H-Index

About

Chang-Sung Jeong is a leading researcher at the intersection of artificial intelligence, distributed computing, and IT convergence, with a focus on developing robust, real-world detection systems. His most cited work, "FaNDeR: Fake News Detection Model Using Media Reliability" (2018, 25 citations), tackles the critical challenge of information integrity in the digital age. By incorporating media source reliability into the detection pipeline, Jeong’s model offers a novel, more nuanced approach to identifying misinformation, addressing a pressing societal need as automated journalism and unreliable sources proliferate. In parallel, his research advances autonomous vehicle safety through "Distributed deep learning platform for pedestrian detection on IT convergence environment" (2020, 5 citations). This work demonstrates his expertise in deploying deep learning across distributed systems, enabling efficient, real-time pedestrian detection for autonomous navigation. Jeong’s contributions are notable for bridging theoretical AI models with practical, high-stakes applications—from safeguarding truth in media to protecting lives on the road. His work exemplifies how distributed deep learning can be harnessed for IT convergence, making him a key figure in developing trustworthy, intelligent systems for modern infrastructure.