Papers
1
Total Citations
5
H-Index
1
About
Fuwen Deng is a rising researcher in the field of artificial intelligence, with a primary focus on multi-agent trajectory prediction and graph-based deep learning. His most notable contribution is the development of the Adaptive Graph Transformer with Future Interaction Modeling, a novel framework that significantly advances the ability to forecast the future paths of multiple interacting agents—such as vehicles or pedestrians—in complex, dynamic environments. This work, published in 2025 and already garnering 5 citations, introduces a mechanism that explicitly models future interactions between agents, moving beyond traditional reactive approaches to enable more accurate and socially compliant predictions. Deng’s research integrates graph neural networks with transformer architectures, offering a powerful tool for applications in autonomous driving, robotics, and intelligent transportation systems. His early impact is evidenced by the rapid citation of his work, signaling its relevance to a pressing challenge in AI. As a young scholar, Fuwen Deng is establishing himself as a key contributor to the next generation of predictive models, with the potential to shape safer and more efficient autonomous systems.
Research Focus
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Top Papers
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