Human Topology

Human topology research explores the structural organization and connectivity patterns within and between different aspects of human systems, ranging from neural networks and skeletal structures to eye movements and even abstract representations of human activity in robot networks. Current research focuses on developing and applying graph convolutional networks, transformers, and Bayesian models to analyze and predict these topological structures, often incorporating biologically-inspired constraints or leveraging topological features for improved performance in tasks like action recognition, pose estimation, and trajectory prediction. This work has significant implications for advancing our understanding of human cognition, improving the efficiency and robustness of artificial systems inspired by human biology, and enabling more accurate and personalized technologies in healthcare and robotics.