Niels is Professor of Health Informatics at the MRC Health eResearch Centre for North England and the Director of the Greater Manchester Connected Health City. He has co-authored over 150 scientific publications in health informatics, artificial intelligence, and epidemiology. His research focuses on improving quality and safety of healthcare using methods and tools from the fields of informatics, statistics and artificial intelligence. He is former President of the Society for Artificial Intelligence in Medicine (AIME). In April 2017, he organised the Informatics for Health 2017 conference in Manchester which was attended by more than 800 people from 30 countries.
Caroline is qualified as both a Psychologist and Computer Scientist and leads the University of Manchester arm of the BBC Data Science Research Partnership. Her research focuses on software engineering and human-computer interaction, taking an inter-disciplinary approach to developing novel technology. A primary area of interest are computational methods to monitor and make sense of complex perceptual processes, providing a window on subconscious cognition, and laying the foundations for technology to improve our decision making capabilities. She is currently developing eye movement analytics that monitor clinical expertise, to assist with the interpretation of medical images such as electrocardiograms (ECGs).
Thomas works primarily on mathematical epidemiology, an area of interdisciplinary applied mathematics that involves a large number of quantitative techniques applied to the study of patterns of disease at the population level. Epidemiology is the science of counting ill people - a simple description that hides major mathematical complexity. A tremendous number of different factors combine to determine the risk that any of us might become ill, however if we could disentangle these from each other then it might be possible to target the most important factors systematically. His work is mainly on the mathematical, statistical and computational methods that are needed to understand these phenomena. Thomas currently works on the EPSRC-funded project 'Operationalising Modern Mathematical Epidemiology'.
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.
Neil’s research interests lie in the areas of applied probability, operations research, optimization/control and game theory. He focuses on resource allocation in random and adversarial environments. For instance, assigning traffic signals under random demand; congestion in internet; and the prediction, assignment and purchase of adverts on search engines. Further, he has interests in financial mathematics as a senior lecturer in Actuarial Sciences and as lecturer in control theory and portfolio optimization in the Financial Mathematics MSc.
Angelo's main research expertise is on language grounding and embodiment in humanoid robots, developmental robotics, human-robot interaction, and on the application of neuromorphic systems for robot learning. He currently is the coordinator of the EU H2020 Marie Skłodowska-Curie European Industrial Doctorate “APRIL: Applications of Personal Robotics through Interaction and Learning” (2016-2019). He also is Principal investigator for the ongoing projects “THRIVE” (US Air Force Office of Science and Research, 2014-1018), the H2020 project MoveCare, and the Marie Curie projects SECURE and DCOMM.
David's research interests focus on building computational models of atmospheric aerosol particles for use in interpretation of measured properties and as sub models for incorporation into climate change models. This broad classification masks a hierarchy of models and techniques with greatly varying complexity and range of applicability. In addition, the research area is highly multi-disciplinary, covering: Physics, Chemistry, Numerical methods and Computational Science.
Goran’s research focuses on unstructured data science, specifically on making sense of large-scale free text data by combing rule-based and data-intensive approaches. His work mainly aims at engineering deep features to train machine-learning algorithms to process free-text documents. His recent and current research projects (funded by NIHR, EPSRC, BBSRC, Welcome, AZ, Pfizer) include large-scale extraction and curation of biomedical information from the literature (including processing table data) and understanding patient free-text data. Goran leads the UK healthcare text analytics network (Healtex), which has been funded by EPSRC to identify the main challenges in processing healthcare free-text.
Hui is a Lecturer in Biostatistics, her previous research was mainly focused on development and applications of statistical methodologies with the aim of understanding the genetics and mechanisms of common complex disease. Currently she is focused on, but not limited to, research on statistical causal inference in Genetics and prediction analysis in longitudinal studies.
Hujun is a senior lecturer at the university. His Research interests include: (1)Theories and applications of neural networks; self-organising systems; deep learning systems (2) Image/video processing, enhancement and recognition; face recognition (3) Nonstationary signal processing; time series analysis and prediction (4) Pattern recognition; data dimensionality reduction and manifold learning (5) Independent component analysis and blind deconvolution (6) Multidimensional data mining and visualisation (7) Neuroinformatics and bioinformatics.
Ian is a Reader in Mathematical Statistics at the university. Prior to his appointment at the School of Mathematics, he led the Bioterrorism and Emerging Disease Analysis programme within the Emergency Response Department. They provide evidence based analysis and advice to Public Health England, Department of Health or other bodies to inform preparedness for, and the response to, the public health threats arising from bioterrorism and new or (re-)emerging infectious diseases. With over 10 years’ experience of modelling emerging diseases and more than 5 years managing a research programme related to modelling and risk assessment, he has 20+ peer reviewed articles related to mathematical modelling of Emergency Preparedness and Response.
Jianxin is a Professor of Statistics within the School of Mathematics. His research spans a number of different topic areas such as jointly modelling mean and covariance structures in longitudinal studies, statistical diagnostics in longitudinal studies, bayesian state-space modelling approaches, generalised linear mixed models, growth curve models and medical statistics, in particular its methodology study involved in randomised controlled clinical trials and epidemiology, including trial designs, pilot studies, sample size calculation and data analysis.
Johan is a Lecturer in Social Statistics at the University of Manchester. He has contributed to the development of a number of statistical models and inference procedures for social networks, in particular exponential random graph models (ERGM) and stochastic actor-oriented models (SAOM). His methodological contributions are often developed in collaboration over substantive research projects with applied researchers and he is active in disseminating best practices through frequent workshops. His current research concentrates on extending current statistical methodology for modelling social interaction to social networks of multiple types of nodes using data collated and collected from different sources. He frequently gives training workshops on statistical methods for social networks to both novices and advanced users of social network analysis and he co-edited a recently published introductory book on ERGM with Dean Lusher and Gary Robins at the Universities of Melbourne and Swinburne.
John Ainsworth is professor of health informatics at the University of Manchester where he is also Director of the Centre for Health Informatics. He is also Deputy Director of the MRC Health eResearch Centre, part of the Farr Institute and is the Director of the Connected Health Cities Coordinating Centre. John works at the intersection of information technology and healthcare research focusing on applying information technology to improving health care and includes: harnessing computing technology to enhance data science, using information technology to improve health services and applying emerging computing technologies to create novel interventions.
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
Ken is a Professor of Epidemiology at the University of Manchester. His main research interests are in translational applications of the epidemiology of cancer and other chronic diseases and healthy ageing. His work in these areas has attracted substantial funding from the UK's leading funders in these conditions together with significant funding from the EU. Ongoing studies include investigations of prostate, breast and ovarian cancers each of which involves the assessment of both environmental and lifestyle exposures (for example diet and occupational history) together with the modifying influences of the genetic make-up of the individual. He is also very interested in the key differences in cancer occurrence between Asian and Western populations. His current work is centred on screening and prevention initiatives in hormonally dependent cancers and other common conditions.
Kody is a professor of Applied Mathematics at the School of Mathematics at the University of Manchester, specializing in computational applied mathematics. He received his PhD in Mathematics in 2010 from the University of Massachusetts, Amherst, and subsequently held positions as a postdoc at the University of Warwick, and senior mathematician at King Abdullah University of Science and Technology and Oak Ridge National Laboratory. He has published in the areas of computational applied mathematics, physics, and dynamical systems. His current research interests are focused on the fertile intersection of mathematics and statistics; in particular, inverse uncertainty quantification: data assimilation, filtering, and Bayesian inverse problems.
Korbinian is a Professor in Statistics at the University of Manchester. He is interested in statistical and machine learning methodology for biomedical data science and how novel statistical and computational approaches and algorithms can aid in the analysis of large-scale complex high-dimensional data. This involves considerable challenges due to the high-dimensionality and complex structure of the data. The biomedical applications of his methods include, e.g., biomarker discovery, clinical diagnostics and systems biology.
Lorenzo works in the Applied Mathematics group of the School of Mathematics. His research interests are in the area of mathematical models for infectious disease dynamics, which include both mathematics and statistics. His current fellowship focuses on developing multi-scale methods to study the epidemiological and evolutionary consequences of co-infection (e.g. when two different pathogens, or variants of the same pathogen, interact within the host).
Nadia is a senior lecturer in Information and Decision Systems. Nadia’s research focuses on improving organisational decision making practices through the use of technologies. This includes research interests in the design, development and evaluation of decision support systems for analysing and improving decision processes. She has served as principal investigator for an EPSRC-funded project on ‘good practice in decision making’, which was in collaboration with British Telecom. She co-authored a Cambridge University Press book 'Decision Behaviour, Analysis and Support'. She has taught on the Full-time MBA, Executive MBA, Masters and specialist executive programmes.
Nick is best known for his work on the accuracy and stability of numerical algorithms. He has more than 130 refereed publications on topics such as rounding error analysis, linear systems, least squares problems, matrix functions and nonlinear matrix equations, condition number estimation, and generalized eigenvalue problems. He has contributed software to LAPACK and the NAG library, and has contributed code included in the MATLAB distribution.
Professor Ser Huang Poon
Professor Poon is internationally renowned for volatility research, Asset Pricing, Derivatives and Credit Risk which has been cited as reference readings on Nobel website. Current projects include Examining the impact of blockchain technology on the behaviour and dynamics of interest rates, market liquidity and market makers and the implication for and impact on Central Bank currency risk management. Theoretical results published to date have been successfully employed in investment practices within the Swiss and German financial systems. Professor Poon’s research has contributed to the development of a new interdisciplinary field, ‘Mathematical Behavioural Finance’ (MBF), dealing with mathematical models of financial markets based on behavioural principles, she is also working with institutional investors on ‘financial innovation and the real economy’.
Simon is a senior lecturer in applied mathematics within the School of Mathematics. His research interests lie in the interface between applied probability, statistics and numerical analysis. He is particularly interested in the development of numerical algorithms which allows him to simulate and analyse complex stochastic systems, ranging from Markov chain Monte Carlo (MCMC) methods for Bayesian inverse problems, to multiscale methods for stochastic biochemical reaction networks.
Sophia is the Director of NaCTeM and leads the Text Mining Research Group. She has led the development of the text mining tools and services currently used in NaCTeM with the aim to provide scalable text mining services: information extraction, intelligent searching, association mining, etc. Her main contributions are in the area of natural language processing, and in particular computational terminology and biomedical text mining. The work in computational terminology and term recognition led to the development of the C-value method for automatic term recognition which has been adopted as a standard method internationally.
Stefan's primary research interests are numerical analysis, in particular, the design and analysis of numerical algorithms for the solution of partial differential equations and related high-dimensional linear algebra problems, rational approximation, scientific computing, and parallel algorithms. He is currently devoted to rational Krylov methods for the efficient solution of Maxwell's equations and nonlinear eigenvalue problems, optimized transparent boundary conditions, and deferred correction methods. In February 2017, Stefan was awarded the People's Vote prize in the Better World Awards for his work on Predictive Alarm Analytics in collaboration with Sabisu.
Tjeerd is a Professor of Health Informatics at the MRC Health eResearch Centre for North England. His current research activities concern randomised trials that use routinely collected data (such as electronic health records). These trials can either randomise patients or practices to different interventions (the latter are known are known as cluster trials). These types of trials could help to answer questions around routinely used interventions and should be conducted with minimal impact on clinicians and patients. Other research activities concern multidatabase research and analysis of quality of electronic health records.
Darren Price is an experimental particle physicist working at the University of Manchester. He currently holds an STFC Ernest Rutherford Fellowship and additionally holds an IPPP Senior Experimental Fellowship. Previously, he was an EU-funded Marie Curie Research Fellow, also at Manchester, and before that was a postdoctoral fellow with Indiana University. He is a member of the ATLAS Collaboration at the Large Hadron Collider, at CERN, Geneva, and the Darkside-20k and Darkside-50 direct dark matter detection experiments based in the Gran Sasso National Laboratory (LNGS) in Assergi, Italy. He is also a member of the DZero Collaboration at Fermilab, Chicago, and previously performed research on the SuperNEMO neutrinoless double beta decay experiment. Darren co-directs the STFC Centre for Doctoral Training in Data Intensive Science (4IR) at the University of Manchester.