Intelligent Modelling and Analysis (IMA)
The Intelligent Modelling and Analysis (IMA) group has established itself as a unique brand in the UK for end-to-end data modelling and analysis. We are a highly interdisciplinary research group focusing on the development of models and techniques for real-world and multifaceted problems in data analysis. We encompass researchers from a variety of backgrounds including computer science, the biomedical sciences, operational research, mathematics, statistics and complexity science.
IMA's main research objectives are: modelling and representation of challenging problems, with particular emphasis on biological, biomedical and digital economy application domains; creating cutting-edge analysis methodologies, both for general purposes and specifically tailored to our main application domains; focussing on difficult, challenging and important real-world problems, with particular emphasis on large and noisy data sets. To undertake this world-leading research, we use a range of techniques including: Machine Learning/Explainable AI; AI-based Data Mining; Bio-Inspired Algorithms; Computational Modelling; Discrete and Agent-Based Simulation.
Dr Jamie Twycross is an interdisciplinary Computer Scientist working at the interface of Computer Science and the biological sciences, and a recognised expert and leader in the interdisciplinary application of AI. He is passionate about developing computational approaches to address hard, real-world biological problems, and his interdisciplinary research reflects his deep interest in understanding how the biological and digital worlds work and interact. He is group lead of the Intelligent Modelling and Analysis Group, and lead the Modelling Group in the Synthetic Biology Research Centre. He has published extensively and my research has been funded by national and international agencies. His research expertise covers computational and mathematical modelling,machine learning and artificial intelligence, and data analytics and visualisation.
Dr Xin Chen’s research interests are image processing, computer vision and machine learning, particularly applied to medical image analysis. His team develops algorithms for 2D/3D image segmentation, image registration, statistical shape/ motion modelling, classification/ regression models and CT & MRI image reconstruction, which have been successfully applied to different medical applications (e.g. breast cancer, diabetes care, wrist injury and radiotherapy). He is an active member in the computer vision and medical imaging community, regularly serve as a reviewer for international conferences and prestigious journals (e.g. Medical Image Analysis, MICCAI, IEEE-ISBI, BMVC, IEEE-TMI, IEEE-TBME, Physics in Medicine and Biology, etc.).
Dr Peer-Olaf Siebers’ main research topic is the application of Computer Simulation and Artificial Intelligence to study human-centric and coupled human-natural complex adaptive systems. He is a strong advocate of using Co-Creation for object oriented Agent-Based Modelling (ABM). This novel and highly interdisciplinary research agenda involves disciplines like Social Science, Economics, Marketing, Psychology, Geography, Operations Research, and Computer Science. His current research is primarily concerned with advancing the model development strategies for ABM, and novel uses of simulation for studying Urban Sustainability, modelling Human Behaviour in Floods using ABM and Virtual Reality, and modelling people's wellbeing at local and global scale.
Dr Chao Chen’s research is focused on the development of artificial intelligence techniques within the area of fuzzy sets and systems for modelling human reasoning, with particular emphasis on optimisation and evaluation towards more robust, efficient and explainable AI.
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