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About

Mariliza Tzes is a rising leader in multi-robot autonomy, specializing in active information acquisition, semantic planning, and manipulation under uncertainty. Her work tackles the fundamental challenge of enabling teams of mobile robots to intelligently gather information in complex, unknown environments. Her most cited paper, "Graph Neural Networks for Multi-Robot Active Information Acquisition" (2023, 38 citations), introduces a scalable framework for coordinating robot teams to estimate hidden phenomena—with applications spanning target tracking, coverage, and SLAM. Tzes also developed SPINE (2025, 8 citations), a system for online semantic planning that allows robots to interpret and execute high-level missions from incomplete natural language instructions without relying on pre-built maps—a critical capability for real-world deployment. Her earlier contributions include distributed sampling-based planning for non-myopic information gathering (2021, 8 citations) and reactive informative planning for mobile manipulation tasks under sensing and environmental uncertainty (2022, 7 citations). Through her innovative integration of graph neural networks, semantic reasoning, and reactive control, Tzes is shaping the next generation of autonomous systems that can perceive, reason, and act in unstructured environments—pushing the boundaries of what multi-robot teams can achieve.