My research interests include collective action, social ecological systems, large-scale software systems engineering, model componentization and coupling, and finding effective ways to promote and facilitate good software engineering practices for reusable, reproducible, and interoperable scientific computation.

Urban Mobility
Machine Learning
Social Network Analysis
Crime Simulation

I obtained a PhD in database information theory from the University of the West of Scotland in 2015, and have been a researcher at the James Hutton Institute ever since. My areas of research are agent-based-modelling (ABM), data curation, effective use of infrastructure as a service (IaaS), and semantic information representation and extraction using formal structures such as computerised ontologies, relational databases and any other structured or semi-structured data representations. I primarily deal with social and agricultural models and was originally taken on in the role of knowledge engineer in order to create the ontology for the H2020 project, Green Lifestyles, Alternative Models and Upscaling Regional Sustainability (GLAMURS). Subsequent work, for the Scottish Government has involved the use of IaaS, more commonly referred to as the “cloud” to create rapidly deployable and cheap alternatives to in-house high-performance computing for both ABM and Geographical Information System models.

It is the mixture of skills and interests involving modelling, data organisation and computing infrastructure expertise that I believe will be highly apposite in the duties associated with being a member of the CoMSES executive. Moreover, prior to joining academia, I spent about 25 years as a developer in commercial IT, in the agricultural, entertainment and banking sectors, and feel that such practical experience can only benefit the CoMSES network.

André Calero Valdez does research on Computational Communication Science investigating the influence of network structure and algorithms on communication flow using agent-based modeling.

My profound interest in networks convinced me to work in these subjects and start my master project on an application of social network analysis for detecting organized fraud in Automobile insurance, which helps to flag groups of fraudsters. The key point of this project is simply to find fraudulent rings, while the most of traditional methods have only taken opportunistic fraud into consideration. My duty in research is to design an algorithm for identifying cyclic components, then to be compared with theoretical ones. This project showed me how networks are used in the analysis of relations.

Muaz is a Senior Member of the IEEE and has more than 15 years of professional, teaching and research experience. Muaz has been working on Communication Systems and Networks since 1995. His BS project in 1995 was on the development of a Cordless Local Area Network. In 1996, his postgraduate project was on Wireless Connectivity of devices to Computers. In addition to his expertise as an Communications engineer, his areas of research interest are in the development of agent-based and complex network-based models of Complex Adaptive Systems. He has worked on diverse case studies ranging from Complex Communication Networks, Biological Networks, Social Networks, Ecological system modeling, Research and Scientometric modeling and simulation etc. He has also worked on designing and developing embedded systems, distributed computing, multiagent and service-oriented architectures.

My main research areas are agent-based modelling, urban analytics and complex decision making enabled by AI. I am interested in the bottom-up transition of complex urban systems under major socio-economic and environmental shocks, such as climate change and the fourth industrial revolution. I want to understand how cities as self-organised complex systems respond to external shocks and evolve under a constantly changing environment. In the past, I have looked at various aspects of urban systems, including the housing market, the labour market, transport and energy system. I am also interested in decision making in complex systems. For example, I have studied the decision to become a vegetarian/vegan under social influence. I have also looked at global food trade in a complex trade network and the resulting food and nutrition security. Recently, I am interested in applying AI algorithms especially reinforcement learning in multi-agent systems, including applications of AI in urban adaptation to climate change, housing market dynamics and criminal behaviour in an urban system.

My research interests concern the computational study of financial markets (microstructure, systemic properties and behavioral bias), of social Interactions on complex networks (theory and experiments), the evolution of cooperation in networks (theory and experiments) and the study of companies strategies in the digital economy.

network science, machine learning, simulation, data science.

The big picture question driving my research is how do complex systems of interactions among individuals / agents result in emergent properties and how do those emergent properties feedback to affect individual / agent decisions. I have explored this big picture question in a number of different contexts including the evolution of cooperation, suburban sprawl, traffic patterns, financial systems, land-use and land-change in urban systems, and most recently social media. For all of these explorations, I employ the tools of complex systems, most importantly agent-based modeling.

My current research focus is on understanding the dynamics of social media, examining how concepts like information, authority, influence and trust diffuse in these new media formats. This allows us to ask questions such as who do users trust to provide them with the information that they want? Which entities have the greatest influence on social media users? How do fads and fashions arise in social media? What happens when time is critical to the diffusion process such as an in a natural disaster? I have employed agent-based modeling, machine learning, geographic information systems, and network analysis to understand and start to answer these questions.