Kadir has pioneered the development of new spike sorting algorithms for electrophysiological experiments which are used by over 300 laboratories worldwide (Rossant, Kadir, et al. 2016 Nature Neuroscience, 19, 634).
Shabnam is a mathematician with a history of interdisciplinary research in computational neuroscience, theoretical physics, pure mathematics and software engineering. Shabnam studied mathematics at Trinity College, Cambridge followed by a DPhil at the Mathematical Institute, Oxford. During her DPhil and subsequent postdoctoral research fellowships at the Fields Institute, Toronto, and the Institute fuer Algebraische Geometrie at Leibniz Universitaet, Hannover, she applied mathematical ideas inspired by string theory to topics in geometry and topology. In particular, she was inspired by the fruitfulness of using computational methods to inspire new and unexpected mathematical conjectures. This led her to start looking at problems in neuroscience during postdoctoral positions at UCL, Imperial, Rutgers and NYU. Her key achievements so far have been in developing machine learning algorithms for the processing and analysis of very large datasets by experimental neuroscientists. She is now a senior lecturer at the University of Hertfordshire and is building new techniques of topological data analysis for use on neuroscientific data, ranging from the auditory system, to vision and olfaction.