Computing Science and Mathematics Seminars,
Spring 2025
Unless otherwise state, seminars will take place in Room 4B96, Cottrell Building, University of Stirling from 13.00 to 14.00 on Friday afternoons, followed by informal discussions.
Spring 2025
| Date | Speaker | Title/Abstract |
|---|---|---|
| Friday 31 January |
Geoffrey Neumann, University of Southampton | Modelling large and diverse populations in marine microbial ecology - an ecological and computing challenge Photosynthetic planktonic microbes (phytoplankton) form the basis of most oceanic food webs and account for almost 50% of the global carbon sink. Computer simulations are essential in understanding the processes driving their distribution and how these may change in a changing climate. In all simulations there exist trade-offs, in particular how we effectively approximate organisms that number around 1027 individuals globally with huge diversity. Some simulations fall into the category of Individual Based Models, with which phylogenies can be tracked enabling us to fully understand the taxonomic diversity within the model, although such models are limited in population size. Population based models, in contrast allow an effectively unlimited population size but without such ability to track diversity. In this I provide a brief introduction to phytoplankton modelling and introduce a novel approach hybridising population and individual based models. I discuss, in particular, what our findings say about the role of population size and what implications this has for the future of modelling.
Speaker bio: Dr Geoffrey Neumann is a Postdoctoral Research Fellow in Modelling Marine Microbial Biodiversity at the University of Southampton. As an ecological modeller, his research focuses on uncovering global patterns in plankton biogeography. Dr Neumann has a background in computer science, he earned a BSc in 2010 and a PhD in 2014, followed by research in the same field at the University of Stirling. In 2022, he obtained an MSc in Oceanography at Southampton. Now, he combines his computer science expertise with his growing knowledge of marine ecology to design and run simulations that model populations of billions of plankton cells circulating the globe, being selected out by the environment, growing, dying, and evolving. |
| Friday 7 February |
||
| Friday 14 February |
||
| Friday 21 February |
||
| Friday 28 February |
|
|
| Friday 7 March |
Reading week |
|
| Friday 14 March |
||
| Friday 21 March |
Piotr Lipiński, University of Wroclaw, Poland | Using hidden data structures in explanation and optimization Modern learning algorithms often construct internal data representations in an auxiliary vector space, called embeddings, that preserve important data patterns, reflect complex relationships between the data samples and reveal some hidden data structures. This talk focuses on using additional knowledge encoded in embeddings in real-world applications, especially related to explanation and optimization. I will discuss modeling relations between frequent patterns in stock market data, discovering seasonalities in forecasting water demand in urban water distribution networks, as well as, constructing explanation rules for session-based recommender systems. Recent representation learning approaches are efficient for solving tasks with complex data structures: multivariate time series processing (e.g. Time2Vec, TRep), sequential or session-based recommender systems (e.g. LightGCN, SRGNN, TAGNN), remote sensing time series (e.g. Presto), etc. Studying such hidden data structures may lead to additional knowledge, not only about the operation of the learning algorithm, but above all about the characteristics of the data themselves, useful in explanation and optimization.
Speaker bio: Piotr Lipiński is an associate professor at the University of Wroclaw (Poland) and the head of the Computational Intelligence Research Group at the Institute of Computer Science. He received a PhD degree from the University of Strasbourg (France) in 2004, a DSc degree (habilitation) from the University of Wroclaw (Poland) in 2018, where he became an associate professor in 2023. His research interests focus on computational intelligence, evolutionary algorithms, machine learning and their real-world applications. |
| Friday 28 March |
Kelechi Samuel Nwosu and Osamu Takahashi |
13:00-13:30 Talk 1: Advancing human body measurement estimation from images using deep learning for commercial applications This research explores deep learning techniques for accurate human body measurement estimation from images, enhancing applications in e-commerce, healthcare, and ergonomics. By leveraging AI and reference objects, it improves precision and commercial viability, advancing the state of the art in computer vision-based measurement Speaker bio: Nwosu Kelechi holds a Bachelor's degree in Mechanical Engineering and has five years of experience in procurement and supply chain management and a year building and running a technology start-up. He is currently completing an MSc in Artificial Intelligence at the CSM department of the University of Stirling, where he focuses on AI-driven solutions. His research interests lie at the intersection of deep learning and computer vision, particularly in applying these technologies to human body measurement estimation for commercial use cases.
13:30-14:00 Talk 2: Exponential analysis of operational deposits Recent advances in exponential analysis have led to considerable success in several signal processing applications. However, its potential impact on financial data analysis remains largely unexplored. We investigate using exponential analysis to assist banks in monitoring their minimum cash reserves, ensuring compliance with the Liquidity Coverage Ratio (LCR) introduced by Basel III in 2008. Prony-based methods decompose financial time series into structural components, identifying depositor activity cycles and their significance. The extracted parameters provide insights into future operational trends, while fluctuations in deposit volumes and spending patterns help assess depositor risk. This work is a collaboration between the University of Stirling (Annie Cuyt, Wen-shin Lee, Anthony O’Hare), and Belgium’s University of Antwerp (Annie Cuyt, Michele Pugno) and KBC Bank (Irem Yaman). Speaker bio: Osamu Takahashi is an early-stage researcher affiliated with the University of Stirling in the EU-funded EXPOWER project. His current research focuses on exploring exponential analysis for financial time series data. He holds a Master’s degree in Economic Management and Policy, with a focus on International Financial Economics, from the University of Strathclyde, and a second Master’s degree in Mathematics and Data Science from the University of Stirling. He has work experience in fund-data infrastructure administration and export-import management, where he applied his knowledge of economics and data management to real-world situations. |
| Friday 4 April |
||
| Friday 11 April |
Simon Power, Computing Science and Mathematics, University of Stirling | . |
Previous Seminar Series
1996:
Autumn
Computing Science and Mathematics
Faculty of Natural Sciences
University of Stirling
Stirling
FK9 4LA
Scotland
UK