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About

Brendan Morris is a leading researcher in autonomous systems and human-robot interaction, with a primary focus on trajectory prediction for safe navigation in crowded environments. His most impactful work, "Convolutional Neural Network for Trajectory Prediction" (2019), has garnered 183 citations, establishing a foundation for efficient, real-time pedestrian motion forecasting essential for self-driving vehicles and social robots. Morris advances this field by integrating semantic scene understanding, as seen in his 2023 work on "Semantic scene upgrades for trajectory prediction," which enhances prediction accuracy by incorporating environmental context. His innovative "STGT: Forecasting Pedestrian Motion Using Spatio-Temporal Graph Transformer" (2021) models inter-pedestrian behavior through graph representations, capturing complex social interactions. Morris’s contributions are critical for enabling autonomous agents to anticipate human movements with both precision and computational efficiency, directly impacting the safety and effectiveness of robots and vehicles operating in dense, dynamic settings. His research continues to push the boundaries of how machines understand and predict human motion in shared spaces.