Xiaoyu Mo

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About

Xiaoyu Mo is a researcher whose work sits at the intersection of complex systems and autonomous driving, tackling challenges of scale, structure, and safety. A key area of Mo’s research is the analysis of large hierarchical networks—structures found everywhere from animal groups and gene networks to engineered systems like smart grids and multi-robot teams. In their highly cited 2023 paper, “The Immense Impact of Reverse Edges on Large Hierarchical Networks,” Mo demonstrated how seemingly minor structural features can fundamentally alter the behavior of these vast systems, a contribution with over 11 citations that is reshaping how engineers design robust, scalable networks. Mo also addresses a critical bottleneck in autonomous driving: predicting the future motion of other vehicles. Their 2022 work on “Deep learning-based interaction-aware trajectory prediction” tackles the complex problem of anticipating driver behavior in real-world, high-stakes scenarios, laying groundwork for safer motion planning. By bridging theoretical network science with practical AI for self-driving cars, Mo’s research offers both foundational insights and direct engineering applications, marking them as a rising voice in systems engineering and intelligent transportation.