This page contains information on people working in UK mathematical systems and control theory. If you would like to add your profile here, then please get in touch.
Chris Guiver
Chris Guiver is a lecturer at Edinburgh Napier University, starting the role in 2020. Before that he was a Lecturer in Applied Mathematics at the University of Bath. Chris obtained a Mmath and Ph.D. in mathematics in 2008 and 2012, respectively, both from the University of Bath. Between 2012 and 2015, he was a postdoctoral researcher at the University of Exeter.
Chris’ research interests lie at the intersection of mathematical analysis and mathematical control theory. He currently performs research in a range of areas, including infinite-dimensional systems, nonlinear control theory, and positive control systems, with application areas ranging from theoretical ecology to renewable energy. His university webpage is linked here.
Chris is a mathematics enthusiast, and one strand of this enthusiasm is as a co-organiser for the annual Royal Institution Edinburgh Mathematics Masterclass series, linked here.
Susana Gomes
Susana N. Gomes is an Associate Professor in Applied Mathematics at the University of Warwick, where she moved in 2018 after a PhD and postdoctoral positions at Imperial College London, and undergraduate and MSc degrees from the University of Coimbra in Portugal.
Her research is specialised in the modelling, inference, and control for real-world problems, in particular problems in the physical, life, and social sciences modelled by partial differential equations. Susana has developed control methodologies in areas such as microfluidics, and has contributed to other areas, such as modelling and control in opinion dynamics and the development of inference methodologies for pedestrian dynamics and cell dynamics. She is involved in mentoring PhD students through being a PhD coordinator in one of Warwick’s doctoral training programmes (Mathematical Modelling for Real World Systems) and is an enthusiastic supporter of early career researcher and women in mathematics initiatives. Her university webpage is linked here.
Boumediene Hamzi
Boumediene Hamzi is a Senior Scientist at Caltech’s Department of Computing and Mathematical Sciences, an Affiliate Fellow of the Data Science Institute at Imperial College London, and an External Researcher at the Alan Turing Institute (London, UK).
Throughout his research career, he has sought to answer the pivotal question: How can complex systems be effectively analyzed? His investigations have branched into three key approaches:
- Dynamical Systems Theory (DST): This approach allows for the analysis of complex systems when the model is known. It offers nontrivial ways to analyze dynamical systems. It has the status of Theory, but it is currently limited to low-dimensional and some classes of infinite-dimensional dynamical systems.
- Machine Learning (ML): ML is concerned with designing algorithms that accomplish tasks, improving as they process more data. It’s particularly useful for analyzing high-dimensional complex systems where the model is unknown. However, its theoretical framework is still missing, and it lacks clear methodologies, making it unclear why certain algorithms work and their domain of applicability.
- Algorithmic Information Theory (AIT): AIT provides a framework for understanding concepts such as complexity, induction, simplicity, randomness, and information content. It’s a robust theoretical approach but faces practical challenges in computing the involved quantities.
His personal webpage is linked here.