The University has developed leading-edge machine learning methods, implemented in widely-used open-source computational packages, to build probabilistic predictive models from large-scale datasets in business, engineering, biology and, particularly, health, in partnership with the Manchester-based Health e-Research Centre.
The group conducts world-leading research into a wide range of techniques and applications of machine learning, optimisation, data mining, probabilistic modelling, pattern recognition and machine perception. They span the field from new theoretical developments to large applications, and are currently supported by a number of research bodies, including EPSRC, BBSRC, and several industry partners.
Jonathan is a Reader in the School of Computer Science and specialises in machine learning, probabilistic modelling and evolutionary algorithms. He is the head of the Machine Learning and Optimization Research Group and his research interests include reinforcement learning and active learning for optimization.
An important focus of Dr. Shapiro’s career has been the training and support of postgraduate and early career researchers. He was Director of the highly prestigious Santa Fe Institute Complex Systems Summer School (2003), created and directed the two EPSRC Ambleside Complex Systems Summer Schools (2006, 2008), and was co-Director of the Budapest Complex Systems Summer School (2002) sponsored by SFI and the Central European University.
Ross is Professor of Machine Intelligence at the University, his research interests include the automation of science, DNA computing, AI, machine learning, drug design, and synthetic biology. He leads the 'Robot Scientist' lab: a Robot Scientist is a physically implemented robotic system that applies techniques from AI to execute cycles of automated scientific experimentation. The lab hosts the second generation Robot Scientist 'Eve', which is designed to automate early-stage drug development: drug screening, hit conformation, and cycles of QSAR hypothesis learning and testing. Eve is focused on neglected tropical diseases, and has discovered 'lead' compounds against malaria that are currently being tested in Cambridge.
Gavin works on aspects of ensemble learning, efficient deep learning, feature selection/extraction, and information theoretic / probabilistic models. He has applied these to domains as diverse as bio-health informatics, adaptive compilers, and humanitarian issues around disaster scenarios. He is currently conducting research on Machine Learning methods for clinical drug trials, methods for predicting domestic violence, and efficient modular deep neural networks, sponsored by the UK and European Union
Dr Chen leads the Lab of Machine Learning and Perception that is affiliated with the Machine Learning and Optimisation Research Group. His main research interests include machine learning, pattern recognition, machine perception, computational cognitive systems and their applications in intelligent system development. Some of his recent work includes zero-shot learning and semantic representation learning and their works were published in International Journal of Computer Vision, Neural Networks and ACM Transactions on Intelligent System and Technology. In addition, he recently proposed a novel procedural content generation framework based on machine learning to generate quality game content and personalised video games.
Further Researchers across the Unviersity:
Magnus is the Director of the Data Science Institute. He works on probabilistic modelling and Bayesian methodology with applications involving large-scale data from high-throughout molecular biology assays. His research group are developing and applying novel statistical methods for the analysis of these datasets.
His group works on how to learn models and make inferences given evidence from high-throughput biological datasets. The models that are developed range from mechanistic differential equation models of the cell to more abstract probabilistic latent variable models that can be used uncover interesting structure in high-dimensional data. They are particularly interested in hybrid models that combine aspects of mechanistic and probabilistic models.
They are applying their methods to infer gene regulatory networks from time-series mRNA expression and DNA-protein binding data, to uncover changes in the transcriptome from RNA-Seq datasets, and to develop novel inference algorithms for time-series data analysis and systems biology modelling.
Julia's research interests relate to the development and application of advanced analytical techniques (concretely, optimization methods, machine learning and simulation) for complex real-world problems, and she has a keen interest in the development and use of these techniques in challenging application areas. Her publications span both theoretical and empirical work related to the multiobjective formulation of a variety of different problems including unsupervised clustering, semi-supervised classification, feature selection and protein structure prediction
As a member of the Text Mining Group she develops Machine Learning techniques in the field of image and text analytics. She constructs effective machine learning models to simulate human intelligence by automating tasks such as matching, recognition, prediction, ranking, inference, characterisation, language and vision understanding.