Yihong Gong

Xi'an Jiaotong University, Fujian Agriculture and Forestry University

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About

Yihong Gong is a leading researcher at the forefront of decentralized machine learning and autonomous systems. His work fundamentally addresses how intelligent systems can learn continuously from distributed data sources—a critical challenge for real-world AI deployment. In his highly influential 2022 paper on Deep Class-Incremental Learning from Decentralized Data (14 citations), Gong pioneered a new paradigm that enables models to adapt to new information without forgetting past knowledge, all while respecting data privacy across multiple repositories. This foundational contribution has significant implications for edge computing and federated learning applications. Gong has also made substantial advances in autonomous driving perception, notably through his 2021 work on panoramic multi-object tracking using multimodality collaboration (12 citations), which overcomes the limitations of single-camera systems by fusing data from multiple sensors. Additionally, his research extends to hardware-level innovations, including a rail-to-rail high-speed comparator for Time-of-Flight (ToF) systems (2023), demonstrating his rare ability to bridge algorithmic breakthroughs with practical hardware implementation. With a research portfolio spanning decentralized learning, multi-object tracking, and sensor systems, Gong continues to shape the future of intelligent, privacy-preserving autonomous systems.