Computing Science and Mathematics Seminars,
Autumn 2024

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.

If you would like to give or suggest a seminar talk, or if you need more information, please contact the seminar organising team lead .

Autumn 2024

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Date Speaker Title/Abstract
Wednesday
9 October
Dalila Hamami, Mostaganem University, Algeria Advancing Machine Learning and Artificial Intelligence for Healthcare and Education: An International Collaboration opportunity

I will explore my current research projects leveraging machine learning and artificial intelligence to address pressing challenges in both healthcare and education. This talk will cover three significant projects:

  • AI for Cancer Detection: Employing machine learning algorithms to enhance early detection and diagnosis of cancer, with the aim of improving patient outcomes and optimizing healthcare resources.
  • Autism Diagnosis Using Machine Learning: Developing AI-driven tools to assist in the early and accurate diagnosis of autism spectrum disorders (ASD), contributing to personalized treatment and better support systems for individuals with ASD.
  • AI-Assisted Language Learning: A project focusing on the application of AI to develop tailored solutions for learning English for specific purposes (ESP), which can help learners acquire language skills in specialized fields like business, technology, or medicine.
In addition to discussing the technical aspects of these projects, I will share insights into my research team and the methodologies they use. The talk will also highlight my broader goals of fostering international collaboration, particularly with UK-based institutions. I aim to establish partnerships for co-supervision of postgraduate students and research, opening pathways for joint innovation in AI applications across borders.

By establishing links between Mostaganem University and Stirling university, this seminar serves as an invitation to UK academics and researchers who are interested in co-developing cutting-edge AI technologies, sharing resources, and contributing to impactful research in healthcare and education.


Speaker bio: Dr Dalila Hamami is a Senior Lecturer in Computing Science at Mostaganem University, Algeria.
Friday
11 October
Wenyang Chu, Virtonomy GmbH, Munich, Germany Accelerating Medical Device Development Through In-Silico Solutions

This talk will explore the transformative potential of in silico (computer-based) simulations in medical device development, focusing on how advanced computational models are revolutionizing the design, testing, and validation of medical devices.

Key topics:

  • The growing integration of in silico methodologies into regulatory submissions, with support from agencies such as the US FDA (Food and Drug Administration) and the UK MHRA (Medicines and Healthcare products Regulatory Agency).
  • Real-world applications, including the use of virtual patients to simulate treatments for Tricuspid and Mitral Valve conditions, optimizing device design while reducing reliance on traditional animal testing.
  • The future of hybrid clinical trials, where virtual trials complement real-world data to create a more efficient and scientifically robust pathway to regulatory approval.
We will offer insights into how computational modeling and AI in academic research can influence medical device innovation and regulatory science. In addition, we will explore how these approaches can reduce development risks, improve clinical trial planning, and accelerate the translation of scientific research into clinical practice.


Speaker bio: Wen-Yang Chu is the Chief Technology Officer and Co-Founder of Virtonomy.io, a pioneering company that applies digital twin technology to healthcare, specializing in both medical implant research and development as well as clinical trials. His work focuses on utilizing AI and biomedical simulations to create virtual patients, enabling the digitalization of clinical trials and accelerating medical device innovation. This innovative approach significantly reduces the reliance on animal testing and shortens timelines for regulatory approval. Virtonomy.io’s cloud-based platform, v-patients.com, integrates medical data, advanced simulations, and AI-driven models to optimize the design, testing, and validation of medical implants and devices.

Wen-Yang holds a European Master of Science in Information and Communication Technologies from Universitat Politècnica de Catalunya and Université catholique de Louvain. His academic background in signal processing, AI, computer vision, and simulation technologies has equipped him with the expertise to drive groundbreaking innovations in healthcare.

With over a decade of industrial experience in high-performance computing, machine learning, and software development, Wen-Yang has a proven track record of translating cutting-edge technologies into practical applications. Before founding Virtonomy.io, he worked as a Data Scientist and Software Architect at Philips, where he developed AI models and scalable image analysis systems for computational pathology. His contributions to medical device development earned him several Philips prestigious awards.

Originally from Taiwan, Wen-Yang now lives in Belgium with his wife and two daughters, aged 10 and 13. In his spare time, he enjoys playing the violin, traveling, hiking, and spending quality time with his family.

Committed to bridging the gap between academia and industry, Wen-Yang continues to push the boundaries of digital twin technology, advancing the development of next-generation medical devices. His interdisciplinary approach, combining AI, simulation, and real-world data, empowers medical device companies to test and validate implants more efficiently and precisely.
Friday
18 October
William Langdon, Department of Computer Science, University College London Evolutionary Robustness

Even in stable conditions biology can retain its ability for continued evolutionary improvement even after 75,000 generations. Instead of 36 years, with performance effectively exceeding a trillion GP operations per second, Genetic programming (GP) experiments can be run to a million generations in weeks on a single computer. Information theory explains why in small populations, GP populations converge and the rate of fitness improvement falls as huge GP trees become more robust to crossover.

Mutation testing on C and C++ programs show that real software can also be robust to many source code changes. As with lisp functional language in tree GP, there is a tendency for deeply nested imperative code to be more robust.

There are already examples of human written software systems that exceed a billion lines of (imperative) source code. Information theory's failed disruption propagation (FDP) helps to explain why maintaining, testing and debugging such deeply nested code repositories is hard and why software companies prefer unit testing of modules (each of which is typically only shallowly nested) rather than system testing of complete functional hierarchies. There is already SBSE work on automatically optimizing test oracles. FDP suggests systems should be built with many densely packed test agents so that disruption caused by bugs has little distance to travel before being discovered by an oracle.

For evolutionary computing and artificial life experiments aiming for sustained innovation, we propose the use of "mangrove" architectures composed of many small trees which are intimate with their environment. For continuous innovative evolution the fitness function needs to be able to measure on average if genetic changes are good or not, or at least have made a difference. This means we must overcome robustness, without introducing chaos. We suggest this might be met by systems where the bulk of the code remains close to the fitness environment and the disruption caused by most mutations and crossovers has only a short depth to propagate in order to have a measurable fitness impact.

Genetic programming and other types of Evolutionary Algorithms have long been demonstrated to be creative. A recently raised question was how much are they used? Data from the genetic programming bibliography for last year suggests 38 ±5% of published papers are primarily on applications which just happen to use GP. Many applications relate to health, civil engineering or solid state materials, e.g. batteries.

Friday
25 October
Reading week

Friday
1 November
Elizabeth Wanner, School of Computer Science and Digital Technologies, Aston University
Friday
8 November
Yuanlin Gu, Computing Science and Mathematics, University of Stirling

Friday
15 November
Friday
22 November
Friday
29 November
Andrew Abel, Computer and Information Sciences, University of Strathclyde
Friday
6 December
Nguyen Dang, School of Computer Science, University of St Andrews

Friday
13 December


Previous Seminar Series

2024:  Spring   Autumn
2023:  Spring   Autumn
2022:  Spring   Autumn
2021:  Spring   Autumn
2020:  Spring   Autumn
2019:  Spring   Autumn
2018:  Spring   Autumn
2017:  Spring   Autumn
2016:  Spring   Autumn
2015:  Spring   Autumn
2014:  Spring   Autumn
2013:  Spring   Autumn
2012:  Spring   Autumn
2011:  Spring   Autumn
2010:  Spring   Autumn
2009:  Spring   Autumn
2008:  Spring   Autumn
2007:  Spring   Autumn
2006:  Spring   Autumn
2005:  Spring   Autumn
2004:  Spring   Autumn
2003:  Spring   Autumn
2002:  Spring   Autumn
2001:  Spring   Autumn
2000:  Spring   Autumn
1999:  Spring   Autumn
1998:  Spring   Autumn
1997:  Spring   Autumn
1996:  Autumn
 

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