Scientific Area: Physics
Languages: Portuguese, English, Spanish, French
Research Interests: Science and design, nature-inspired design, morphogenesis, mathematical and computational modelling, complex systems, topology optimization, generative design, machine learning, data analysis and visualisation.
Filipa Alves holds a degree in Biology from the University of Lisbon (FCUL) and a PhD in Physics from Instituto Superior Técnico (IST-UL). She also studied Computer Programming, Applied Mathematics, Machine Learning, Additive Manufacturing and History of Science. Filipa is an experienced teacher, having coordinated and taught courses at different levels of education.
As a biophysicist, her research combines mathematical modelling and image analysis with interdisciplinary collaborations to investigate the mechanisms underlying the generation of spatial and temporal patterns and forms in living organisms. Filipa has studied morphogenesis from different points of view: from the construction of form during embryonic development to the evolutionary origin and diversification of morphologies. In these contexts, we can identify key interaction networks governing the functional responses of cells (or organisms) to internal and external stimuli in a wide variety of signalling—sensing—response conditions. Filipa has been focusing on the properties of these interaction networks as complex systems, analysing their temporal and spatial dynamics, parameter sensitivity, robustness to varying conditions, adaptability, potential for self-organisation and pattern formation, among others.
The study of morphogenesis and of interaction networks in the living world has led her, in recent years, to a growing interest in topological optimisation and generative design. Taking as a source of knowledge and inspiration the mechanisms responsible for the generation of form in nature, Filipa is currently exploring the wide potential for synergy between the investigation of natural and artificial systems. Her research goal is to apply a complex systems approach to develop and test new optimisation algorithms for the generative design of products and systems of different natures and scales.