Group Members

Dr Mahir Arzoky Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London. Prior to this he was a Post-doctoral Research Fellow working on a research project titled Assessing the Quality of Test Suites in Industrial Code (AQUATIC - EPSRC: EP/M024083/1). He was a core member of the Fault Analyses in Industry and Academic Research Network (FIAR-NET - EPSRC: EP/N011627/1), where he promoted collaborations between industry and academic research in software engineering through a series of national and international workshops. Prior to this, he was a Research Associate in Machine Learning at the Cognitive Digital System Engineering Centre, Birmingham City University, where he worked on a collaborative Data-driven Smart City project that aimed to simplify complex decision making, informing policy and strategic service developments using unified data, and state of the art machine learning simulation and modelling. He was also part of an Innovation Engine project, part-funded by the European Regional Development Fund, that aimed at stimulating demand for new or improved services, processes and products from local SME businesses and start-up companies by bringing and helping to solve existing challenges within the Life Sciences, Digital and Creative sectors. Dr Arzoky obtained his PhD from the Department of Computer Science at Brunel University London in 2015. His research interest lies in the areas of Artificial Intelligence, Intelligent Data Analysis, Data Mining and Software Engineering, in specific Search Based Software Engineering. Artificial Intelligence, Intelligent Data Analysis, Heuristic Search, Search Based Software Engineering, Clustering, Refactoring

Dr stasha Lauria Dr Stanislao Lauria has a Laurea awarded by The University of Studies “Federico II” of Napoli in Italy. He holds a Ph.D. in Cybernetics from The University of Reading, UK. Dr Stanislao Lauria is a Lecturer at Brunel University London. Previously he was research fellow at the University of Plymouth and at the University of Reading. Dr. S. Lauria has been working in the area of intelligent robotics for more than 15 years, and is particularly specialised in modelling and training mobile robots by means of intelligent human-machine interactions. Specifically, he has investigated the use of various frameworks for representing knowledge and converting natural language into robot-understandable actions. He has established the Brunel Robotics Laboratory performing exploratory experiments on cognitive mobile robots. He has also investigated the use of various Artificial Intelligence paradigms for various signal processing domains. His current activities focus on Machine-Human interactions. In particular, he is investigating the implication of Social Media on Human-Robot interactions and dialogue management aspects. Finally, he is exploring the use of robotics as an educational tool. Neural Networks Pattern Recognition/Processing Natural Language based interactions Dialogue Based systems. Multi Agent Architectures. Robotics Machine-Human Interactions Social Network and Machine-Human interactions Big Data searches. Dr Lauria has been involved in designing delivering and assessing several teaching modules with a particular emphasis on programming. Therefore, suitable methods to allow students at a beginner level to increase their confidence in programming have been introduced. The aim has been to both introduce alternative paradigms to stimulate student motivation and to increase student’s perception of their own skills. As part of various outreaching programs Dr. Lauria has developed some innovative methods based on short interactive sessions to allow naive user to be able to control and program robots. Teaching areas. Computer Networks Programming Languages Database Software Engineering Database Algorithms

Dr Yongmin Li Please visit my personal website where you may find more details of my work. Dr. Yongmin Li received his PhD from Queen Mary, University of London, MEng and BEng from Tsinghua University, China. Before joining Brunel University, he worked as a research scientist in the British Telecom Laboratories. Dr. Li is a Senior Member of the IEEE, and Senior Fellow of the Higher Education Academy. He was ranked in the world's top 2% scientists by the Standardized Citation Indicators (Elsevier) every year over the past four years ( 2020, 2021, 2022, and 2023). His research interest covers the areas of data science, machine learning, artificial intelligence, image processing, computer vision, video analysis, medical imaging, bio-imaging, biomedical engineering, healthcare technologies, automatic control and nonlinear filtering. Together with his colleagues, their work has won the following awards: 1st Place, RETOUCH Challenge (Online), MICCAI 2023 (with Ndipenoch, Miron and Wang). 2nd Place, FeTA Challenge, MICCAI 2022 (with McConnell, Ndipenoch and Miron). Most Influential Paper over the Decade Award, MVA, 2019 (with Ruta, Porikli, et al). Best Student Paper Award, Bioimaging, 2018 (with Dodo, Eltayef and Liu). VC Prize, Brunel University, 2015 (with Kaba and Liu). Best Paper Award, HIS, 2012 (with Salazar-Gonzalez and Kaba). Best Poster Prize, BMVC, 2007 (with Ruta and Liu). Best Scientific Paper Award, BMVC, 2001 (with Gong and Liddell). Best Paper Prize, RATFG, 2001 (with Gong and Liddell). Prospective PhD Students: We invite talented and hard-working students to join us for their PhD study. From time to time, we may have studentships available, which include an annual bursary (about £18,000 this year) plus payment of tuition fees for three years. Currently we have several projects on-going, for example, Deep Learning for Medical Imaging, Natural Language Processing for Business Intelligence, Natural Language Processing for Tax Assessment, and Image/Video Content Generation for Personalised Remarketing. But any other topics within the area of artificial intelligence and data science would also be welcome. Contact me for details if interested. Master of Science in Artificial Intelligence 2023/24: Built on our strong international research profile (consistently ranked in the top 100 in the world, 1st in UK for H-index and Highly Cited Papers for 3 years in a row from 2018-2020, and 3rd in UK for overall performance in "the NTU Performance Ranking of Scientific Papers for World Universities", Subject: Computer Science, 2020), we offer the MSc Artificial Intelligence course with great flexibility (1 year full-time, 2 year part-time or 3 year staged study). If you are interested, apply here. 15 Scholarships available for applicants from under-represented groups, £10,000 of each. Research & Development Collaboration: Developing downstream applications of large AI models is a focused area of my group in the upcoming years. Contact me if you have a collaboration project. We can assist in securing funding from sources like UKRI, EU, or Innovate UK, potentially cutting your costs in the project significantly (e.g. by 1/3 or more), plus the university's input. Please visit my personal website where you may find more details of my research. Computer vision, image processing, video analysis, medical imaging, bio-imaging, machine learning, pattern recognition, automatic control and nonlinear filtering. CS0002 Introduction to Programming CS5707 Artificial Intelligence CS5708 Deep Learning

Professor Xiaohui Liu Xiaohui Liu is Professor of Computing at Brunel University London where he conducts research in artificial intelligence, data science and optimization, with applications in diverse areas including biomedicine and engineering. Xiaohui has held senior visiting positions in Leiden, Harvard and Chinese Academy of Sciences, advised UK Research Councils on data analytics, genomics and security as well as the Royal Statistical Society/the Institute and Faculty of Actuaries on statistical education at UK schools in light of big data. Professor Liu founded the international symposium series on Intelligent Data Analysis (1995 & 1997), served on the international panel to assess the quality of computer science research in the Netherlands (2015-16), and since 2014, he has been named as a Highly Cited Researcher for 10 consecutive years in Computer Science, Engineering, or Cross-Field (Clarivate/Web of Science). Professor Liu has been an investigator on a number of grants (see below) in research areas including AI, bioinformatics, complex networks, data science, deep learning, engineering and manufacturing, healthcare, optimisation, sentiment analysis, and statistical pattern recognition. Modules led at Brunel include: Algorithms and Their Applications Artificial Intelligence Research Methods Software Development and Management Modules supported include: Advanced Topics in Computer Science Data Management & Business Intelligence Distributed/Predictive Data Analysis Final Year Projects Machine Learning Modern Data MSc Projects Year 2 Group Projects

Dr Alina Miron Alina is a lecturer in the Computer Science department and a member of the Intelligent Data Analysis (IDA). Alina has a PhD in machine learning in the field of autonomous vehicles and is an artificial intelligence researcher, developer and educator. She has excellent understanding of data, especially real-time data and a strong background in computer vision, natural language processing and data science. Computer Vision; Medical imaging; Time series data

Dr Alessandro Pandini My research activity focuses on the development and application of computational methods to study protein dynamics and its role in protein-ligand binding, protein-protein interactions, and protein design. I obtained my PhD in Computational Chemistry at the University of Milan-Bicocca under the supervision of Prof. Laura Bonati. As part of her research group I contributed to the unveil the molecular mechanism of toxic response mediated by binding of dioxins to the Aryl hydrocarbon Receptor. In 2008 I was awarded a Marie Curie Inter European Fellowship to work at the MRC National Institute for Medical Research (NIMR) under the supervision of Dr. Willie R. Taylor and Dr. Jens Kleinjung. From 2011 to 2014 he was a BBSRC-funded postdoctoral research assistant in the group of Prof. Franca Fraternali at King’s College London working on methods to investigate allosteric regulation, and to analyse protein-protein interaction interfaces and networks. During my career I developed and applied novel approaches combining structural bioinformatics and molecular simulation to address challenging biological questions, especially in relation to protein function, allosteric regulation and drug design. I introduced novel points of view in the definition of the limits and potential of molecular docking on theoretical models and in the use of molecular dynamics for drug design and medicinal chemistry. In particular, I developed an innovative computational method to detect local functional motions and to describe allosteric transmission in protein structures. Most recently, in collaboration with Dr. Arianna Fornili (QMUL), I contributed to the development of a novel strategy for biasing the sampling of local states to drive the global conformational transitions in proteins. In collaboration with Dr. Shahid Khan (LBNL – Berkeley Lab) and Dr. Willie Taylor, I have contributed to explain the relationships between residue coevolution and molecular dynamics in two bacterial ring assemblies.

Dr Annette Payne Having gained a PhD in 1992 in Molecular Biology from the Royal Postgraduate Medical College, University of London I undertook two post doctorial positions; the first at the National Heart and Lung Institute, University of London researching the molecular biology of atherosclerosis, the second at The Institute of Ophthalmology, University of London researching the molecular genetics of retinal diseases, before joining Brunel University London Dept. of Bioscience in 2000. I have since transferred to the Department of Computer Science as my interest in computational biology grew. I have now additional research interests in technology and computer assisted learning, and the use of technology to monitor and manage medical conditions. I have over 100 peer reviewed publications many of them can be found on my Research Gate profile at These publications highly cited over 2000 times by other researchers (RG score of over 35 most months, and a ISI h-index of 31). I a Research Interest Score that is higher than 92% of researchers in my field. Disciplines Data Mining Human-computer Interaction Computing in Mathematics, Natural Science, Engineering and Medicine Bioinformatics Molecular Biology Systems Biology Skills and expertise Human Genetics Next Generation Sequencing Gene Expression Genomics Transcriptomics E-Learning Blended Learning TEL Synthetic Biology Bioinformatics and Computational Biology My research interests are divided into two areas: 1. Computational and systems biology, including machine learning, bioinformatics and medical informatics. 2. Technology assisted learning, including e-learning, blended learning, cross discipline use of technology in the arts and design. As well as supervising PhD students in my areas of interest I teach the following modules: Ethics and Governance Data and Information Management Group projects Dissertations

Professor Martin Shepperd Martin Shepperd received a PhD in computer science from the Open University in 1991 for his work in measurement theory, many sorted algebras and their application to empirical software engineering. He was seconded to the Parliamentary Office of Science & Technology. Presently he is Head of Department and holds the chair of Software Technology and Modelling at Brunel University London, UK. He has published more than 150 refereed papers and three books in the areas of software engineering and machine learning. He is a fellow of the British Computer Society. Previously Martin has worked as a software developer for HSBC. Software engineering, Empirical research, Cost modelling and prediction, Machine learning (including case-based reasoning, metaheuristics, rule induction algorithms and Grey relational algebra), Data imputation and noise handling, Reproducibility, replicability and meta-analysis. Introductory data science (CS5702 Modern Data) to the MSc students and Research methods to the doctoral students (CS5767).

Dr Stephen Swift Dr. Stephen Swift is a Research Lecturer in the School of Information Systems, Computing and Mathematics at Brunel University London. He received a B.Sc. degree in Mathematics and Computing from the University of Kent, Canterbury, U.K., an M.Sc. in Artificial Intelligence from Cranfield University, Cranfield, U.K. and a Ph.D. degree in Intelligent Data Analysis from Birkbeck College, University of London, London, U.K. He has four years post-doctoral research experience on an EPSRC funded project entitled “Modelling Short Multivariate Time Series” (involving Moorfields Eye Hospital) GR/M94120) and a BBSRC funded project entitled “Analysing Virus Gene Expression Data to understand Regulatory Interactions” (BIO14300) in collaboration with the Departments of Virology and Biochemistry at University College London and the School of Computer Science and Information Systems, Birkbeck College. He has also spent four years in industry as a web designer, programmer and technical architect. Research interests include multivariate time series analysis, heuristic search, data clustering, and evolutionary computation. He has applied his research to a number of real world areas including Software Engineering, Bioinformatics and Health Care.

Dr Allan Tucker Allan Tucker is Reader in the Department of Computer Science where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analytics for 21 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He was academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS, and another on detecting significant changes in Adaptive AI Models of Healthcare (£195k). He is currently academic lead on two Pioneer Funds on Explainability of AI (£168k) and In-Silico Trials (£750k). He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics. He hosted a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019 and was general chair for AI in Medicine 2021. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia. Data Mining / Data Science Machine Learning Artificial Intelligence Bayesian Networks Big Data Biomedical Informatics Eco Informatics I have designed and led the following modules: Business Intelligence (MSc) - at Brunel (~150 students) and NITH, Oslo (~30 students) for 1 year. Machine Learning (MSc) - at Brunel (~10 students) for 3 years. Logic and Computation (Level 1) - at Brunel (~200 students) for 4 years. Artificial Intelligence option (level 3) - at Brunel (~200 students) for 4 years. High Performance Computational Infrastructures (MSc) - at Brunel (~30 students) for 1 year. Other teaching: • JAVA programming (level 1) - at Brunel (~200 students) for 5 years. • Masters level Statistics course - at Brunel Graduate School (~10 students) for 1 year.

Professor Zidong Wang Zidong Wang is a member of Academia Europaea, a Member of the European Academy of Sciences and Arts, an IEEE Fellow and Professor of Computing at Brunel University London, UK. He has research interests in intelligent data analysis, statistical signal processing and dynamic systems & control. He has been named as the Hottest Scientific Researcher in 2012 in the area of Big Data and listed as highly cited researchers in categories of both computer science and engineering in 2015-2020 with an h-index of 139. He is currently serving as the Editor-in-Chief for International Journal of Systems Science, the Editor-in-Chief for Neurocomputing, the Editor-in-Chief for Systems Science and Control Engineering, and Associate Editor for other 12 prestigious journals including 5 IEEE Transactions. His research has been funded by the EU, the Royal Society and the EPSRC. Intelligent Data Analysis (Data modelling, Data mining, Data classification, Data quality evaluation, Neural Networks, Fuzzy systems, Statistical identification), Statistical Signal Processing (Digital filter design, Envelope-constrained filter, Signal processing for uncertain systems, Optimal filtering and deconvolution, Multi-rate and filter banks), Dynamical Systems and Control (Stochastic control, Robust control and estimation, H-infinity control, Model reduction, Sampled-data systems, Time-delay systems, Nonlinear systems, Multi-dimensional systems, Fuzzy control, Robot control). Introduction to Computing, Artificial Intelligence, Data and Information, Construction of Programs, Software Engineering Methods

Dr Fang Wang Dr Fang Wang is a Senior Lecturer in the Department of Computer Science at Brunel University London. She received a PhD in artificial intelligence from the University of Edinburgh and worked as a senior researcher in the research centre of British Telecom (BT) Group, before she joined Brunel University London in 2010. Dr. Wang has published a number of papers in books, journals and conferences and filed a series of patents. Dr. Wang is an established teacher and researcher in computer science and artificial intelligence. Her research interests include nature-inspired computing, agents, intelligent information processing, intelligent distributed computing, cognitive radio networks, e-learning and cloud education, cognitive science and computer vision. She actively participated in a number of EU, EPSRC, BT long term research projects and received several technical awards, including the Gordon Radley Technical Premium Highly Commended award of BT and ACM Best Student Paper Award at the Third International Conference on Autonomous Agents. She is on the editorial boards of several international journals and serves on many program committees. Dr. Wang’s main research interest is in artificial intelligence and its applications. This includes using nature-inspired techniques such as intelligent agents, swarm intelligence, evolutionary computing and neural networks to solve real world applications such as network optimisation, radio spectrum management, decentralised computing, user analysis, self-organising communities, and so on. Lectured, administered, tutored and examined courses at undergraduate and MSc levels on topics including Introduction to programming, Algorithms and their applications, Systems in Context, Digital Innovation, level 1 and level 2 group projects and final year projects. Class sizes varied from 8 to 350. Supervised a number of undergraduate and MSc projects.

Professor Keming Yu Keming Yu – Chair in Statistics Research Director (Impact) – in Mathematical Sciences 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. Based on mathematical theory and data analysis methods, my research aims to explore statistical methods, models and optimal algorithms to deal with challenges in: New regression models and methods, including quantile regression, for Financial Econometrics and Business. Robust algorithms for Machine Learnign and Deep Learning. Statistical analysis and Machine learning for modelling loneliness and social isolation in Gerontology. New distributional/regression methods for the analysis of Wellbeing, health and biomedical scoences, such as obesity. Statistics/Machine learning methods for risk assessment in engineering, such as rail truck failure, cable fault, pipeline corrosion and wind turbine. Statistical theory, method, including Bayesian analysis, for the analysis of big data and small data. I teach Level 3 UG Statistics and MSc Statistics Courses. And I supervise final year UG student projects and MSc dissertation. MA3670: Statistics III. MA5632MA5673: Computer Intensive Statistical Methods. MA5629MA5676: Time Series Modelling.