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About

Jonathan Shapiro is a researcher whose work sits at the intersection of machine learning, autonomous robotics, and neural network architectures. His most influential contribution is the development of self-organising neural networks, most notably his 2002 paper "A Self-Organising Network that Grows When Required," which has accumulated an impressive 387 citations and remains a cornerstone reference in adaptive network design. Alongside this, Shapiro made significant advances in autonomous robot mapping, with two complementary 2002 papers on globally consistent map-building — together drawing nearly 250 citations — that addressed the critical challenge of correcting drift errors in odometry-based navigation through innovative relaxation and online learning techniques. A recurring theme throughout Shapiro's career is novelty detection: equipping mobile robots with the capacity to recognise and respond to unfamiliar stimuli in unstructured environments. Beginning around 2000, he published a series of foundational papers exploring habituation-inspired algorithms and self-organised normality models, establishing a coherent and influential research program in this area. His 2005 paper on online novelty detection extended this work with real-time capabilities. Collectively, Shapiro's research has profoundly shaped how autonomous robots perceive, learn from, and adapt to dynamic environments.