The EXPOWER project (led by Annie Cuyt, University of Antwerp) combines a broad spectrum of key research and training activities on Multi-Exponential Analysis with applications in industry, that are currently being undertaken in some premier research institutes. The network is interdisciplinary, intersectoral, unconventional and ambitious. It is unconventional in the sense that it connects stakeholders from seemingly separately developed fields: computational harmonic analysis, numerical linear algebra, computer algebra, nonlinear approximation theory, digital signal processing and their applications, in one and more variables. It is ambitious because the consortium stretches from mathematics to computational science and engineering and industry.

[2020-2023] (RAEng/GCRF: £300K) Multimodal Data Analysis For Monitoring Invasive Aquatic Weeds In India

The aim of this project is to develop effective methods that combine the use of multiple data sources (satellite and drone observations and ground-based sensors) to monitor the spread of invasive aquatic weeds in neglected and inaccessible water bodies in India. The project focuses on water hyacinth (Eichhornia crassipes) in the Kuttanad region of Kerala.

[2019-2022] (ST Microelectronics/Univresity of Stirling: £143.75K) PhD studentship: Domain specific optimisations for real-time image processing on heterogeneous FPGA+CPU+GPU

[2019-2023] (Scotland's Rural College/Univresity of Stirling: £79K) PhD studentship: Improving animal health in marine ecosystem using data mining and signal/image processing

[2018-2022] (ESRC: £370K) Identifying Novel Markers of Concealed Face Recognition

The project develops new ways through eye-tracking, physiological responses and micro-expressions, to detect recognition of familiar faces when people deny recognition of someone they know. It combines the Concealed Information Test (CIT) with theoretical models of familiar and unfamiliar face recognition to create novel and simple techniques that have the potential for use in a wide range of security settings.

[2015-2021] (EPSRC: £6.1M) Face Matching for Automatic Identity Retrieval, Recognition, Verification and Management

FACER2VM is a five-year research programme aimed at making face recognition ubiquitous. The project will develop unconstrained face recognition technology for a broad spectrum of applications. The approach adopted will endeavour to devise novel machine learning solutions, which combine the technique of deep learning with sophisticated prior information conveyed by 3D face models.