Dr Paul McMenemy

I am a Lecturer within the Division of Computing Science and Mathematics at the University of Stirling, where I am an active member of the Data Science (DAIS) and Mathematical Biology (BioMod) Research Groups. I am a Lecturer in Pure Math/Mathematical Modelling, with research interests in mathematical modelling of biological systems and diseases. My main research interest are in developing models that will help industry stakeholders further understand how sewage discharges impact levels of norovirus in shellfish and how this can be mitigated against to maximise food safety while optimising processes and profits for shellfish farmers. I also work in the fields of combinatorial optimisation and information theory. I am also working on publications in the field of bioeconomics (a model describing the interactivity between farming intensity and biosecurity) - all in collaboration with Professor Adam Kleczkowski of the University of Strathclyde, as well as other subject matter experts in the fields of aquaculture, mathematics and economics.

I returned to education in 2007 when I enrolled in the Access to Higher Education programme at Stirling, and since then have successfully gained a 1st Class Honours in Mathematics & Computing Science, and a PhD in Mathematics. I recently completed 2 years working as a Post-Doctoral Fellow as part of the DAASE project, before taking up my current post of Lecturer.

My research objectives are to publish high quality journal papers, and I am currently working on further papers from the doctoral thesis work I carried out on the modelling of water-borne pathogens within the shellfish industry. These papers build a strongly linked mathematical framework of the transmission of water-borne pathogens from point/non-point pollution sources into farmed shellfish. I am also currently collaborating on novel multi-objective optimisation techniques, as well as macro-economic modelling of attitudes towards biosecurity in global shrimp farming. I also have a strong impetus to further explore the topic of my 2012 dissertation "A Process To Determine All Codewords of a Binary (n,M,d)-Code". This work defined a process for the construction of an optimal codeword alphabet, M, from a specific n-length binary code with Hamming distance d, where optimality is a maximum value of M which has an optimal error detection and correction capability, as well as a minimal code block length n. There are numerous methods that can be applied to a search for the optimal alphabet, and discovery of the most efficient method would be of great academic interest.

I am interested in learning and applying new search methodologies such as hyper-heuristics, genetic programming and matheuristics, which are possible jumping-off approaches to determine the optimal codeword alphabet for a specific binary code.

I am also interested in undertaking research into how climate change has affected the frequency and intensity of rainfall in specific geographic locations, something which impacts not only how wastewater plants process increased volumes of sewage, but how this also has direct effects landscape erosion and the economies of impacted shellfish farming and other industries along coastal regions.

I have experience in lecturing bothMathematics and Computing Science subject areas, teaching many undergraduate & postgraduate modules in my time at Stirling.

My 2024 spring/summer modules are Stochastic Processes & Optimization, and Mathematical Foundations (PGT) as well as providing supervision for dissertation students from both Computing/Data Science and Mathematics MSc programmes. I am also writing a new Mathematics UG module for Spring 2025, Data Science Research, and act as Liaison Officer with SUMS, the student-led Stirling University Maths Society.

Previous modules I have enjoyed teaching include Databases, Graph Theory & Networks (PGT), Time Series & Stochastic Processes, Linear Algebra, Discrete Structures, Data Skills (all UG).

Between 2013-17 (during my PhD), I was the Course Co-ordinator for the Data Skills module, a part of the Access to Higher Education programme at the University.