Pau Closas

Northeastern University, Universitat Politècnica de Catalunya, Centre Tecnologic de Telecomunicacions de Catalunya

Papers

Total Citations

108

H-Index

About

Pau Closas is a leading researcher at the intersection of signal processing, Bayesian inference, and intelligent systems, with a focus on localization, navigation, and haptic perception. His work advances the theoretical and practical foundations of Bayesian filtering for indoor tracking, particularly in environments where GNSS signals are unreliable. In his highly cited 2012 paper (19 citations), he experimentally validated Bayesian filtering techniques for ultra-wideband sensor networks, establishing robust methods for indoor localization. Closas also pioneered crowd-based learning for spatial field inference in the Internet of Things (28 citations), enabling context-aware applications through data harvesting from distributed sensors. More recently, his 2020 work (55 citations) integrated Bayesian and neural approaches with LSTM networks for multimodal object recognition using tactile and kinesthetic information, pushing the boundaries of robotic touch sensitivity. This work exemplifies his ability to bridge classical probabilistic methods with modern deep learning. With a citation impact spanning over a decade, Closas’s contributions are foundational to autonomous navigation, IoT sensing, and intelligent robotics, making him a key figure in the evolution of perception and inference systems.