Xiangzheng Zhou

Nanjing University of Science and Technology

Papers

Total Citations

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About

Xiangzheng Zhou is a rising researcher in artificial intelligence, with a primary focus on multi-agent trajectory prediction and heterogeneous graph learning. His most-cited work, the "Heterogeneous hypergraph transformer network with cross-modal future interaction for multi-agent trajectory prediction" (2025), has already garnered 12 citations, signaling its early impact in the field. Zhou's key contribution lies in developing novel transformer-based architectures that integrate heterogeneous hypergraph structures with cross-modal interaction mechanisms, enabling more accurate and socially-aware predictions of multiple agents' future paths. This work addresses critical challenges in autonomous driving, robotics, and crowd simulation, where understanding complex, dynamic interactions among diverse agents is essential. By modeling both spatial and temporal dependencies through hypergraph transformers, Zhou advances the state-of-the-art in trajectory forecasting, offering a robust framework for handling heterogeneous data sources. His research is particularly notable for its emphasis on cross-modal future interaction, which allows the model to anticipate how agents influence each other's trajectories over time. As a young scholar, Zhou's early citation success and innovative approach position him as a promising contributor to the next generation of intelligent, interactive AI systems.