Professor Marian Gheorghe,

Emeritus Professor - FOEI

Research

Professor Gheorghe's research interests are in computational models and software engineering. He is interested in various computational models like rewriting systems - formal grammars, multiset rewriting -, automata, process algebras and Petri nets. He has a special interest in new classes of unconventional computational models, especially membrane systems and their connections with other (unconventional) computational models - Petri nets, process algebras, L-systems, reactive systems etc. He is also interested in formally verifying such models by using model checking approaches. Professor Gheorghe is also interested in model-based testing and connections with formal verification. He has been working with large scale simulations, based on formal models, and contributed to building adequate software platforms for such approaches. He is also interested in applications of formal specifications and formal verification methods and tools in computational and synthetic biology, but also in autonomous systems. He served as the Chair of the Steering Committee of the Conference on Membrane Computing between 2010 - 2014, the main research forum of the membrane computing community, editing the proceedings of these events with Springer, in Lecture Notes in Computer Science Series. He has connections and active collaborations with many prestigious Universities and research groups in the world, being invited to give lectures and providing guidance to PhD students. Professor Gheorghe's research has been funded by EPSRC, EU, Royal Academy of Engineering, Royal Society, British Council and has been included in some international projects abroad - China and Romania - as scientific advisor and collaborator.

Research projects

Research projects involving Professor Marian Gheorghe.

Role	Date	Title/description	Funder	Award
PI	2022-03-31T00:00:00	The project is aimed at creating a novel formal verification and model based testing approach for a class of nature inspired computation, called spiking neural P systems – a special type of artificial intelligence spiking neural computing model, that is applied in data-driven classification, pattern recognition, signal processing and optimisation problems. This approach will contribute to the development of learning models that are correct by construction and efficient with respect to certain complexity metrics. The project will make extensive use of the complementary skills of the applicants and their teams. The UK partner will bring expertise in model verification and testing, whereas the co-applicant will contribute with complexity and computational aspects of the investigated model. Both partners have expertise in computational modelling, in particular P systems. The outcomes of the project will have a significant impact on each of the two partner institutions by creating a new research thread in natural computing, especially membrane computing, allowing to develop artificial intelligence applications fully validated through novel formal verification and model-based testing methods. New computational and complexity measures will be also investigated. These results will impact on further developments in membrane computing and use of artificial intelligence methods in data-driven applications. Young researchers and PhD students of each partner institution will benefit from an international collaboration that is aimed to continue beyond the time limit of the project.		PI