Talk
Liquid crystals are classical examples of mesophases or partially ordered materials that combine fluidity with the ordering characteristics of solids. Liquid crystals are well known for their applications in the electro-optic industry, photonics, sensors, energy harvesting, healthcare technologies and smart materials. In this talk, we briefly review the physics and mathematics of liquid crystals, including the essential continuum theories of liquid crystals and their underpinning mathematical framework. We then illustrate the connections between mathematical modelling and real-life liquid crystal applications with concrete examples, thus describing the three-way feedback loop between rigorous theory, scientific computation and applications.

Bio
Apala Majumdar is a professor of applied mathematics at the University of Manchester, UK. Apala received her PhD in applied mathematics from the University of Bristol in 2006, followed by postdoctoral fellowships at the University of Oxford and took up her first faculty position at the University of Bath in 2012. Apala was appointed a Global Talent Attraction Platform Professor of Applied Mathematics at the University of Strathclyde in 2019, and she moved to the University of Manchester in 2025. Apala is an internationally acclaimed applied mathematician with expertise in mechanics, modelling, calculus of variations and nonlinear partial differential equations. Apala’s research programme is highly international and interdisciplinary and has been recognised with multiple national and international awards. Notably, Apala was awarded a Friedrich Wilhelm Bessel research award by the Humboldt Foundation in 2022. Apala was elected Fellow of the Royal Society of Edinburgh in 2024.

Email
apala.majumdar@manchester.ac.uk

Talk
If a manifold M admits metrics g and g' these metrics are projectively related if the geodesics associated with their Levi-Civita connections are identical. In this presentation, I will be discussing the general techniques available for studying the relationships between these connections and metrics. Additionally, I will introduce the Weyl projective tensor for such geometries and show its invariance under projective relatedness; present the Sinjukov transformation for handling such projectively related geometries; and discuss some applications of this theory to Einstein's general relativity.

Bio
Graham Hall graduated from the University of Newcastle upon Tyne, England, receiving his B.Sc in 1968 and PhD in 1971. He has been a member of the staff at the University of Aberdeen since 1973 and was apointed professor in 1996. Since then, he has successfully supervised 20 PhD students and many M.Sc students, in addition to delivering invited research seminars at many universities in North and South America, Europe, Asia, Africa and Australasia. He was elected to a fellowship of The Royal Society of Edinburgh in 1995, followed by a fellowship of The Royal Astronomical Society in 2003. He is currently Emeritus Professor at Aberdeen.

Email
g.hall@abdn.ac.uk

Talk
Image completion is a challenging task, particularly when ensuring that the generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model that ensures intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion.

Bio
Dr. Huiyu Zhou received his Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and his Master of Science degree in biomedical engineering from the University of Dundee of United Kingdom, respectively. He was awarded the Doctor of Philosophy degree in computer vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou is currently a full Professor at the School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He has published over 600 peer-reviewed papers in the field. His research work has been or is presently being supported by UK EPSRC, ESRC, AHRC, MRC, EU, Innovate UK, Royal Society, British Heart Foundation, Leverhulme Trust, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry. Homepage: https://le.ac.uk/people/huiyu-zhou

Email
Hz143@leicester.ac.uk