Prof. Keming Yu

Brunel University of London, UK

Prof.Keming joined Brunel University London in 2005. Before that he held posts at various institutions, including University of Plymouth, Lancaster University and the Open University. Keming got his first degree in Mathematics and MSc in Statistics from universities in China and got his PhD in Statistics from The Open University, Milton Keynes.

His research focuses on Statistics and Data Science, as well as their broad applications and interdisciplinary collaborations.

Prof. Huiyu Zhou

University of Leicester, UK

Dr. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou currently is a full Professor at 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 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 

Title: Image completion with context-adaptive diffusion.

Abstract of the talk:

Image completion is a challenging task, particularly when ensuring that 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 to ensure that 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.

Prof. Man Qi

Canterbury Christ Church University, UK

Dr Man Qi is a Reader in Computing at Canterbury Christ Church University, UK with over 20 years teaching and research experience in computing.  Dr Qi is a Fellow of British Computer Society (FBCS) and Fellow of Higher Education Academy (FHEA).  Her research interests are in the areas of Intelligent Computing and Applications. Dr Qi has over 80 research publications including 40 journal papers. She is an editorial board member for several international journals and has served as program committee member/chair for over 50 international conferences.

Title: AI and Digital Forensics

Abstract: Artificial Intelligence (AI) is rapidly transforming many aspects of our lives, and digital forensics is no exception. From analysing vast amounts of digital evidence to detecting sophisticated cyberattacks, deepfakes, and other AI-enabled crimes, AI is reshaping both the opportunities and challenges faced by digital forensic investigators. In the talk, Dr. Qi will provide an overview of how AI is being applied in digital forensics, discuss some of the latest research and real-world applications, and highlight the challenges, including explainability, bias, data privacy, and the admissibility of AI-generated evidence. Finally, future research directions and how AI can support more efficient, accurate, and trustworthy digital investigations will be discussed.