Alessia Bertugli

University of Modena and Reggio Emilia, University of Trento

Papers

Total Citations

107

H-Index

About

Alessia Bertugli is a researcher specializing in human motion prediction and trajectory forecasting, with a particular focus on developing intelligent systems capable of navigating complex, human-centric environments. Her work sits at the intersection of deep learning, graph neural networks, and probabilistic modeling, addressing one of the most challenging problems in autonomous systems research: anticipating how people move in crowded, dynamic settings. Her most celebrated contribution, DAG-Net (Double Attentive Graph Neural Network), introduced a sophisticated dual-attention mechanism for trajectory forecasting, garnering 58 citations and establishing her as a notable voice in the field. Building on this foundation, her work on AC-VRNN demonstrated compelling advances in multi-future trajectory prediction by leveraging attentive conditional variational recurrent neural networks, acknowledging the inherently multi-modal nature of human movement — an essential capability for socially aware robots and intelligent transportation systems. Bertugli's research carries clear real-world significance, directly informing the development of self-driving vehicles, advanced video surveillance, and autonomous social robots. With a growing citation record reflecting the community's engagement with her ideas, she represents a promising contributor to the rapidly evolving field of human behavior understanding and AI-driven mobility systems.