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About

Wennie Tabib is a robotics researcher whose work sits at the intersection of autonomous exploration, probabilistic mapping, and information-theoretic planning. Best known for pioneering the use of Gaussian Mixture Models (GMMs) in robotic perception, Tabib has fundamentally advanced how robots build compact, high-resolution representations of complex environments. Her 2018 paper on variable resolution occupancy mapping using GMMs (65 citations) established a new paradigm for flexible, memory-efficient spatial modeling, while her 2019 work on real-time information-theoretic exploration extended these representations to enable adaptive decision-making in communication-constrained settings. Tabib has also made significant contributions to planetary exploration robotics, developing kinodynamic planning frameworks for mapping caves, pits, and subsurface tunnels — environments of profound scientific interest on the Moon and Mars. Her 2016 exploration framework for complex concavities (37 citations) demonstrated real-time performance in particularly challenging three-dimensional geometries. More recently, her research has expanded to multimodal surface mapping and multi-robot aerial systems, reflecting a sustained commitment to high-fidelity, scalable autonomy. Across her career, Tabib's work consistently bridges theoretical rigor with practical deployment in some of the most demanding environments imaginable.