Kyeong-Sik Park

Papers

Total Citations

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About

Dr. Kyeong-Sik Park is a researcher at the forefront of applying deep learning to autonomous systems, with a primary focus on synthetic data generation for driving environments. His most cited work, "Efficient Driving Scene Image Creation Using Deep Neural Network" (2019), addresses a critical bottleneck in autonomous vehicle development: the need for vast, diverse, and labeled training datasets. By pioneering methods to generate realistic driving scene imagery through deep neural networks, Dr. Park has contributed to reducing the reliance on costly, real-world data collection. This foundational paper has garnered 5 citations, reflecting its relevance in the rapidly evolving field of machine learning for autonomous navigation. Beyond this, his research intersects with broader advancements in AI-driven classification and robotics, where his work supports the creation of safer, more robust perception systems. Dr. Park’s contributions are particularly notable for enabling scalable simulation environments, a key enabler for testing and validating autonomous vehicles under varied conditions. His efforts continue to shape how researchers approach the synthesis of training data, making him a valuable voice in the intersection of computer vision, generative models, and intelligent transportation systems.