Shan Zheng Tan

Papers

Total Citations

H-Index

About

Shan Zheng Tan is a rising researcher at the forefront of generative AI and human behaviour modelling. His work explores how diffusion models—typically used for image generation—can be repurposed to imitate complex, multimodal human actions in sequential environments. In his highly cited 2023 paper, *Imitating Human Behaviour with Diffusion Models*, Tan demonstrates that these models can capture the stochastic, structured correlations inherent in human decision-making, offering a powerful new approach for robotics and interactive AI. With 23 citations in under two years, this work has quickly become a reference point for researchers bridging generative modelling and imitation learning. Tan’s contributions are notable for their conceptual elegance: rather than treating behaviour as deterministic, he embraces its natural variability, enabling more realistic and adaptable agents. His research sits at the intersection of machine learning, cognitive science, and human-computer interaction, promising to shape how AI systems learn from and collaborate with people. For students and researchers, Tan’s work offers a compelling glimpse into the future of human-aware artificial intelligence.