Agius
harry has three decades of research and teaching expertise in various aspects of digital media, games and creative computing. he serves as deputy editor-in-chief of the multimedia tools and applications journal (springer) and is also the section editor for track 4 (digital games, virtual reality, and augmented reality). personalisation of digital media and digital games using creative computing techniques, particularly ai-based methods digital media and digital games harry has taught a wide variety of subjects in digital media, games and creative computing during his career. his current teaching responsibilities are in the areas of digital experiences, digital futures and emerging technologies, and responsive web development.
![Dr Harry Agius]()
Dr Harry Agius
Harry has three decades of research and teaching expertise in various aspects of digital media, games and creative computing. He serves as Deputy Editor-in-Chief of the Multimedia Tools and Applications journal (Springer) and is also the Section Editor for Track 4 (Digital Games, Virtual Reality, and Augmented Reality). Personalisation of digital media and digital games using creative computing techniques, particularly AI-based methods Digital media and digital games Harry has taught a wide variety of subjects in digital media, games and creative computing during his career. His current teaching responsibilities are in the areas of digital experiences, digital futures and emerging technologies, and responsive web development.
Daylamani-Zad
damon is a senior lecturer in creative computing (ai and games). applications of ai in games and digital media machine learning & evolutionary algorithms (neural networks, genetic algorithm, swarm intelligence) games, serious games and gamification accessibility design in games and digital media extended reality (xr) and immersive technologies: ar, vr and mr computer generated music damon's main research activities are within the creative computing, games and digital media domain, and are specifically focused on applications of artificial intelligence in games and digital media including collaborative content modelling, serious gaming, immersive technologies in cultural heritage, immersive technologies in training and education, gamification for accessibility design and user modelling and personalisation. he has worked on using machine learning for automated music generation, strategy planning, user modelling and game personalisation, use of artificial intelligence (swarm intelligence) in strategic and serious games, use of gamification for accessibility design and serious games for reading interventions in dyslexia. he has been involved in various projects funded by the epsrc, ahrc, nih, the bikeability trust and department for transport. the results of his projects have been adapted for personalisation in mmogs (artemis) and in developing frameworks for collaborative decision-making games (lu-lu and responsive lu-lu).
![Dr Damon Daylamani-Zad]()
Dr Damon Daylamani-Zad
Damon is a Senior Lecturer in Creative Computing (AI and Games). Applications of AI in Games and Digital Media Machine Learning & Evolutionary Algorithms (Neural Networks, Genetic Algorithm, Swarm Intelligence) Games, Serious Games and Gamification Accessibility Design in Games and Digital Media Extended Reality (xR) and Immersive Technologies: AR, VR and MR Computer Generated Music Damon's main research activities are within the Creative Computing, Games and Digital Media domain, and are specifically focused on Applications of Artificial Intelligence in Games and Digital Media including Collaborative Content Modelling, Serious Gaming, Immersive technologies in cultural heritage, Immersive technologies in training and education, Gamification for accessibility design and User Modelling and Personalisation. He has worked on using Machine Learning for automated music generation, strategy planning, user modelling and game personalisation, use of Artificial Intelligence (swarm intelligence) in strategic and serious games, use of gamification for accessibility design and serious games for reading interventions in Dyslexia. He has been involved in various projects funded by the EPSRC, AHRC, NIH, the Bikeability Trust and Department for Transport. The results of his projects have been adapted for personalisation in MMOGs (Artemis) and in developing frameworks for collaborative decision-making games (Lu-Lu and responsive Lu-Lu).
Li
maozhen li is a professor in the department of electronic and electrical engineering at brunel university of london. he received his phd from the institute of software, chinese academy of sciences in 1997. he did post doctoral research in the school of computer science and informatics, cardiff university in january 1999- january 2002, joined brunel in february 2002 as a lecturer and became a professor in september 2013. his research interests are in the areas of high performance computing, big data analytics and artificial intelligence with applications to smart grid, smart manufacturing and cybersecurity. he has over 240 scientific publications in these areas including 5 books with about 8000 citations (google scholar h-index 47). he has served over 30 ieee conferences. he is an associated editor of the journal of cloud computing advances, systems and applications, springer, international journal of grid and high performance computing, international journal of distributed systems and technologies, and journal of computing and informatics. he has successfully secured a number of research grants from funding sources including epsrc, the european union, innovate uk. he is a fellow of the iet and the british computer society. his research work on big data modelling on large scale road networks was shortlisted by computing uk in may 2018 for big data excellence awards in the category of most innovative big data solution. as the first supervisor, he has successfully supervised 25 phd students since he joined brunel university in 2002. as an external examiner, he has examined over 30 phd theses at a number of universities in the uk including university of warwick, university of bath, cardiff university, university of surrey, university of kent, de montfort university. he was an external examiner for msc programme in computing at edge hill university, uk for a period of 3 years in 2019-2022. currently he serves as an external examiner for the bsc business and it at university of malta. high performance computing - looking at computing technologies such as grid computing, peer-t-peer computing, cloud computing and edge computing with an aim to solve data intensive applications in the cloud or at the edge of the network. parallel machine learning techniques - improving computation efficiency of traditioanl machine learning techniques with parallel computing techniques, to speed up the training process on large datasets using multiple cpus and gpus. ai interpretation and robusness - ai models are normally running in a back-box mode and are fragile to real life out-of-distribution data sets. as a result, it is highly risky to deploy ai techniques to life criticial applications such as automonous driving systems. this work aims to develop interpretable and robust ai models which not only provide details on the decision-making process but also have the ability to sense the changes of an external environment. a focus will be on causal ai, a branch of ai which models cause-effect relationships, moving beyond correlation-based traditional ai predictions that can explain why things happen. lightweigh ai models - ai models have continuously growing in sizes having billions of hyperparameters which restrict them from deployment on computing resource constrained devices like robots and drones. this work looks at techniques such as pruning, quantisation, knowledge distillation, and lightweight architectures with an aim to reduce the computation complexity of ai models. i teach both undergraduate and postgraduate modules, with 0.5 fte in the department of electronic and electrical engineering: ee2648 data networks and security (fheq level 5) ee3619 advanced computing technologies (fheq level 6) ee5622 communication network technologies (fheq level 7)
![Professor Maozhen Li]()
Professor Maozhen Li
Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London. He received his PhD from the Institute of Software, Chinese Academy of Sciences in 1997. He did Post Doctoral research in the School of Computer Science and Informatics, Cardiff University in January 1999- January 2002, joined Brunel in February 2002 as a lecturer and became a Professor in September 2013. His research interests are in the areas of high performance computing, big data analytics and artificial intelligence with applications to smart grid, smart manufacturing and cybersecurity. He has over 240 scientific publications in these areas including 5 books with about 8000 citations (Google Scholar h-index 47). He has served over 30 IEEE conferences. He is an Associated Editor of the Journal of Cloud Computing Advances, Systems and Applications, Springer, International Journal of Grid and High Performance Computing, International Journal of Distributed Systems and Technologies, and Journal of Computing and Informatics. He has successfully secured a number of research grants from funding sources including EPSRC, the European Union, Innovate UK. He is a Fellow of the IET and the British Computer Society. His research work on Big Data Modelling on Large Scale Road Networks was shortlisted by Computing UK in May 2018 for BIG DATA EXCELLENCE AWARDS in the category of Most Innovative Big Data Solution. As the first supervisor, he has successfully supervised 25 PhD students since he joined Brunel University in 2002. As an external examiner, he has examined over 30 PhD theses at a number of universities in the UK including University of Warwick, University of Bath, Cardiff University, University of Surrey, University of Kent, De Montfort University. He was an external examiner for MSc Programme in Computing at Edge Hill University, UK for a period of 3 years in 2019-2022. Currently he serves as an external examiner for the BSc Business and IT at University of Malta. High performance computing - looking at computing technologies such as grid computing, peer-t-peer computing, cloud computing and edge computing with an aim to solve data intensive applications in the cloud or at the edge of the network. Parallel machine learning techniques - improving computation efficiency of traditioanl machine learning techniques with parallel computing techniques, to speed up the training process on large datasets using multiple CPUs and GPUs. AI interpretation and robusness - AI models are normally running in a back-box mode and are fragile to real life out-of-distribution data sets. As a result, it is highly risky to deploy AI techniques to life criticial applications such as automonous driving systems. This work aims to develop interpretable and robust AI models which not only provide details on the decision-making process but also have the ability to sense the changes of an external environment. A focus will be on Causal AI, a branch of AI which models cause-effect relationships, moving beyond correlation-based traditional AI predictions that can explain why things happen. Lightweigh AI models - AI models have continuously growing in sizes having billions of hyperparameters which restrict them from deployment on computing resource constrained devices like robots and drones. This work looks at techniques such as pruning, quantisation, knowledge distillation, and lightweight architectures with an aim to reduce the computation complexity of AI models. I teach both undergraduate and postgraduate modules, with 0.5 FTE in the Department of Electronic and Electrical Engineering: EE2648 Data Networks and Security (FHEQ Level 5) EE3619 Advanced Computing Technologies (FHEQ Level 6) EE5622 Communication Network Technologies (FHEQ Level 7)
Liu
xiaohui liu has held several senior visiting positions, including honorary pascal professor at leiden university (2004), visiting scientist at harvard medical school (2005), and visiting professor at the chinese academy of sciences (2010). a long-standing advocate of intelligent data analysis (ida), he has conducted research in artificial intelligence, data science, and optimisation, as well as their integration to enable effective data interpretation and trustworthy decision-making. his work has delivered real-world impact across diverse domains. professor liu has been named a clarivate highly cited researcher for 11 consecutive years (2014–2024), spanning the fields of computer science, engineering, and cross-field research. he has been listed among the world’s top 2% of scientists by stanford university (2020–2025), and recognised by scholargps as a highly ranked scholar – lifetime: neural network (2022–2025). his recent honours include the daniel berg award (2023), the research.com united kingdom leader award in computer science (2023–2025), and the ida founders award (2025). intelligent data analysis, deep learning, dynamical systems, human factors, innovative ai applications, optimisation, statistical pattern recognition, and trustworthy decision making.
![Professor Xiaohui Liu]()
Professor Xiaohui Liu
Xiaohui Liu has held several senior visiting positions, including Honorary Pascal Professor at Leiden University (2004), Visiting Scientist at Harvard Medical School (2005), and Visiting Professor at the Chinese Academy of Sciences (2010). A long-standing advocate of Intelligent Data Analysis (IDA), he has conducted research in artificial intelligence, data science, and optimisation, as well as their integration to enable effective data interpretation and trustworthy decision-making. His work has delivered real-world impact across diverse domains. Professor Liu has been named a Clarivate Highly Cited Researcher for 11 consecutive years (2014–2024), spanning the fields of Computer Science, Engineering, and Cross-Field research. He has been listed among the world’s top 2% of scientists by Stanford University (2020–2025), and recognised by ScholarGPS as a Highly Ranked Scholar – Lifetime: Neural Network (2022–2025). His recent honours include the Daniel Berg Award (2023), the Research.com United Kingdom Leader Award in Computer Science (2023–2025), and the IDA Founders Award (2025). Intelligent data analysis, deep learning, dynamical systems, human factors, innovative AI applications, optimisation, statistical pattern recognition, and trustworthy decision making.
Meng
hongying meng is a full professor in the department of electronic and electrical engineering at brunel university of london. before joining brunel, he held research positions in several uk universities including university college london (ucl), university of york, university of southampton, university of lincoln, and university of dundee. he received his bsc, msc and ph.d. degree in communication and electronic systems all from xi’an jiaotong university, xi'an, china. after that, he worked as a postdoc researcher and then a lecturer at electronic engineering department of tsinghua university, beijing, china before coming to uk. his research area includes signal processing, computer vision, affective computing, artificial intelligence, neuromorphic computing and internet of things. his research is funded by epsrc, eu horizon 2020, royal academy of engineering, royal society, etc. he has published more than 200 academic papers with more than 9000 citations (google scholar h-index 42). he has developed 2 different emotion recognition systems that won avec2011 and avec2013 international challenge competitions respectively. he is an ieee senior member since 2017 and an associate editor for ieee transactions on circuits and systems for video technology (tcsvt) and ieee transactions on cognitive and developmental systems (tcds). he is also an associate editors-in-chief for digital twins and applications (iet). he was recognized as one of the ai 2000 most influential scholars by aminer in 2022 and top scholar (within the top 0.5% of all scholars worldwide) by scholargps in 2024. he was also listed as a top 2% scientist of the world (stanford/elsevier, single-year data sets) in 2023, 2024 and 2025 and one of the world's best scientists by research.com in 2025. digital signal processing: wavelet transform; digital filtering; statistical signal processing; audio signal processing; mechanical signal processing (fault detection), biomedical signal processing (e.g. ecg, eeg, emg, gsr); real-time signal processing. machine learning: support vector machine (svm); kernel methods; artificial neural networks; genetic algorithm; genetic programming, feature selection and fusion; bayesian methods; hidden markov model (hmm); deep learning; long short term memory (lstm), convolutional neural network (cnn), generative adversarial network (gan), transformer, reinforcement learning, self-supervised learning, large language model (llm), multi-label classification; statistical learning theory; multi-score learning, multiple classifier system, decision fusion, data mining, regression, spiking neural networks, neuromorphic computing. human computer interaction: affective computing; emotional states recognition; facial expression analysis; multi-model interaction; movement modelling; gesture recognition, ubiquitous and pervasive computing; robot; self-driving car, ambient intelligence; multimodal emotional interaction system; interactive film; and virtual reality (vr). computer vision: biologically inspired vision systems; dynamic motion feature extraction; human action recognition; object detection; object tracking; visual surveillance; image compression; large scale image categorization; image segmentation; real-time image processing; medical image processing (ct, fmri); embedded vision systems; 3d image processing, holoscopic imaging; autonomous driving systems. embedded systems and communications: fpga; microcontroller (pic, arm); dsp (ti); smart phones; tablet; game consoles, internet of things (iot), digital twin, system on chip (soc), risc-v, controller area network (can), wireless networks and communication (zigbee, bluetooth, ook, visible light communication, mmwave communication). microcontroller principles (fheq level 5) computer architecture and interfacing (fheq level 5) engineering group design project (fheq level 5) advanced embedded systems design (fheq level 7, meng & msc) artificial intelligence system techniques (fheq level 7, meng & msc)
![Professor Hongying Meng]()
Professor Hongying Meng
Hongying Meng is a full professor in the Department of Electronic and Electrical Engineering at Brunel University of London. Before joining Brunel, he held research positions in several UK universities including University College London (UCL), University of York, University of Southampton, University of Lincoln, and University of Dundee. He received his BSc, MSc and Ph.D. degree in Communication and Electronic Systems all from Xi’an Jiaotong University, Xi'an, China. After that, he worked as a postdoc researcher and then a lecturer at Electronic Engineering Department of Tsinghua University, Beijing, China before coming to UK. His research area includes signal processing, computer vision, affective computing, artificial intelligence, neuromorphic computing and Internet of Things. His research is funded by EPSRC, EU Horizon 2020, Royal Academy of Engineering, Royal Society, etc. He has published more than 200 academic papers with more than 9000 citations (Google Scholar h-index 42). He has developed 2 different emotion recognition systems that won AVEC2011 and AVEC2013 international challenge competitions respectively. He is an IEEE Senior Member since 2017 and an associate editor for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) and IEEE Transactions on Cognitive and Developmental Systems (TCDS). He is also an associate Editors-in-Chief for Digital Twins and Applications (IET). He was recognized as one of the AI 2000 Most Influential Scholars by Aminer in 2022 and Top Scholar (within the top 0.5% of all scholars worldwide) by ScholarGPS in 2024. He was also listed as a Top 2% Scientist of the World (Stanford/Elsevier, single-year data sets) in 2023, 2024 and 2025 and one of the World's Best Scientists by Research.com in 2025. Digital Signal Processing: wavelet transform; digital filtering; statistical signal processing; audio signal processing; mechanical signal processing (fault detection), biomedical signal processing (e.g. ECG, EEG, EMG, GSR); real-time signal processing. Machine Learning: Support Vector Machine (SVM); kernel methods; artificial neural networks; genetic algorithm; genetic programming, feature selection and fusion; Bayesian methods; Hidden Markov Model (HMM); deep learning; Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), transformer, reinforcement learning, self-supervised learning, Large Language Model (LLM), multi-label classification; statistical learning theory; multi-score learning, multiple classifier system, decision fusion, data mining, regression, spiking neural networks, neuromorphic computing. Human Computer Interaction: affective computing; emotional states recognition; facial expression analysis; multi-model interaction; movement modelling; gesture recognition, ubiquitous and pervasive computing; robot; self-driving car, ambient intelligence; multimodal emotional interaction system; interactive film; and virtual reality (VR). Computer Vision: biologically inspired vision systems; dynamic motion feature extraction; human action recognition; object detection; object tracking; visual surveillance; image compression; large scale image categorization; image segmentation; real-time image processing; medical image processing (CT, fMRI); embedded vision systems; 3D image processing, Holoscopic imaging; autonomous driving systems. Embedded Systems and Communications: FPGA; microcontroller (PIC, ARM); DSP (TI); smart phones; tablet; game consoles, Internet of Things (IoT), Digital Twin, System on Chip (SoC), RISC-V, Controller Area Network (CAN), wireless networks and communication (ZigBee, Bluetooth, OOK, visible light communication, mmWave communication). Microcontroller Principles (FHEQ Level 5) Computer Architecture and Interfacing (FHEQ Level 5) Engineering Group Design Project (FHEQ Level 5) Advanced Embedded Systems Design (FHEQ Level 7, MEng & MSc) Artificial Intelligence System Techniques (FHEQ Level 7, MEng & MSc)
Nilavalan
professor r. nilavalan obtained the b.sc. eng. in electrical & electronic engineering (first class) from university of peradeniya, srilanka in 1995 and his phd in near-field microwave imaging from university of bristol, uk in 2001. from 1999 to 2005 he was a researcher at centre for communications research (ccr), bristol university working in the field of radio frequency engineering. he was member of the european commission, network of excellence on antennas from 2002 - 2005. he joined brunel university london in september 2005 as a lecturer in wireless communications and currently a professor. professional memberships and services fellow of the iet senior member of the ieee fellow of the higher education academy 5g and beyond communication systems, antennas and propagation, beamforming and phased arrays, emergency communication systems microwave systems use of radio and microwave frequencies in automotive and biomedical applications for past and present projects please refer the personal web wireless communication systems, radio frequency and microwave systems, non-destructive testing and sensing radio and optical communication systems (ee5550/ee5150, msc) advanced electronics (ee3049/ee3601) communication systems (ee2640, level 5) electronic systems (ee2604, level 5)
![Professor Nila Nilavalan]()
Professor Nila Nilavalan
Professor R. Nilavalan obtained the B.Sc. Eng. in Electrical & Electronic Engineering (First Class) from University of Peradeniya, SriLanka in 1995 and his PhD in Near-field microwave imaging from University of Bristol, UK in 2001. From 1999 to 2005 he was a researcher at Centre for Communications Research (CCR), Bristol University working in the field of Radio Frequency Engineering. He was member of the European commission, Network of Excellence on Antennas from 2002 - 2005. He joined Brunel University London in September 2005 as a lecturer in wireless communications and currently a professor. Professional Memberships and Services Fellow of the IET Senior member of the IEEE Fellow of the Higher Education Academy 5G and beyond Communication Systems, Antennas and Propagation, Beamforming and Phased Arrays, Emergency Communication Systems Microwave Systems Use of Radio and microwave frequencies in Automotive and Biomedical applications For past and present projects please refer the personal web Wireless Communication Systems, Radio Frequency and Microwave Systems, Non-destructive testing and sensing Radio and Optical Communication Systems (EE5550/EE5150, MSc) Advanced Electronics (EE3049/EE3601) Communication Systems (EE2640, Level 5) Electronic Systems (EE2604, Level 5)
Serrano-Rico
dr. alan serrano is a reader in the department of computer science at brunel university london, uk, where he also received his phd in information systems and an msc in data communication systems. he is now the chair of the industry advisory board, department industry lead & brunel talent marketplace director. previous to his appointment at brunel he has worked in industry and public sectors in mexico for a number of years, in the areas of computer networks and information systems development. his research interest lies in the area of information systems and organisational strategy. more specifically, dr serrano focuses on undertaking research that aims to solve the real-life challenges organisations face when adopting information and communication technologies (ict) in complex environments. some examples of collaboration with industry includes bmw group in uk, axa insurance uk, pepsico latin america, the national health service (nhs) in uk, centrica energy uk, jaguar & land rover uk, and standard chartered uk. dr serrano has exposed his work in more than 26 publications in recognised journals and international conferences such as the european journal of information systems, electronic markets, the international journal of information management, and the international journal of enterprise information systems. i would not like to box myself into a specific area, as i believe the information systems domain expands in many directions and i find all of these fascinating. i have done research on health care, programme management, and business process and simulation to mention a few. most of my formal research however, lies within the area of information system and business strategy. today i am very passionate about finding effective ways for dissemination of academic knowledge to wider audiences; applying academic research in real context (industry) and the social network phenomena in general. during my academic career, i have taught a number of subjects ranging from the social (is in context) to the technical (java programming, erps, and telecommunications). today my teaching responsibilities are: module leader for cs1703 data and information the aim of this module is to provide students with a comprehensive introduction to different kinds of data and the means by which it can be collected, stored, retrieved, analysed and then communicated in order to achieve the goal of satisfying user information needs. module teaching contributor for cs2006 business analysis and process modelling and cs3072 computer science fyp
![Dr Alan Serrano-Rico]()
Dr Alan Serrano-Rico
Dr. Alan Serrano is a Reader in the Department of Computer Science at Brunel University London, UK, where he also received his PhD in Information Systems and an MSc in Data Communication Systems. He is now the Chair of the Industry advisory board, Department Industry Lead & Brunel Talent Marketplace Director. Previous to his appointment at Brunel he has worked in industry and public sectors in Mexico for a number of years, in the areas of computer networks and information systems development. His research interest lies in the area of information systems and organisational strategy. More specifically, Dr Serrano focuses on undertaking research that aims to solve the real-life challenges organisations face when adopting information and communication technologies (ICT) in complex environments. Some examples of collaboration with industry includes BMW group in UK, AXA Insurance UK, PepsiCo Latin America, the National Health Service (NHS) in UK, Centrica Energy UK, Jaguar & Land Rover UK, and Standard Chartered UK. Dr Serrano has exposed his work in more than 26 publications in recognised journals and international conferences such as the European Journal of Information Systems, Electronic Markets, the International Journal of Information Management, and the International Journal of Enterprise Information Systems. I would not like to box myself into a specific area, as I believe the Information Systems domain expands in many directions and I find all of these fascinating. I have done research on health care, programme management, and business process and simulation to mention a few. Most of my formal research however, lies within the area of information system and business strategy. Today I am very passionate about finding effective ways for dissemination of academic knowledge to wider audiences; applying academic research in real context (industry) and the social network phenomena in general. During my academic career, I have taught a number of subjects ranging from the social (IS in context) to the technical (Java Programming, ERPs, and Telecommunications). Today my teaching responsibilities are: Module leader for CS1703 Data and Information The aim of this module is to provide students with a comprehensive introduction to different kinds of data and the means by which it can be collected, stored, retrieved, analysed and then communicated in order to achieve the goal of satisfying user information needs. Module teaching contributor for CS2006 Business Analysis and Process Modelling and CS3072 Computer Science FYP
Swash
dr rafiq swash joined the department of electronic and computer engineering, college of engineering, design and physical sciences, brunel university london, uk in 2013. before that, he held research positions in the area of multimedia search & retrieval systems, expert systems and 3d imaging & display systems at brunel university london. he received his ph.d. degree in holoscopic 3d imaging systems: camera / processing / display from brunel university london, uk. he is a member of ieee, iet, ieee broadcast technology society, the optical society america (osa), and the society for information display (sid). in the past, dr swash has worked as a senior software engineer, senior technical architect, technical director and chief technology officer in international gaming, financial and robotics industries. dr swash has a wide research interests includes 3d imaging and display systems (3d cameras / 3d display / 3d processing), 3d virtual reality / augment reality, 3d computer vision, medical image processing & visualisation, multimedia search & retrieval, human-computer interaction design, interactive game design including serious games and gamification advanced 3d imaging systems: holoscopic and multiview 3d displays, holoscopic 3d cameras, 3d image processing 3d image reformatting advanced 3d computer graphics multiscopic and stereoscopic 3d systems 3d visual engineering / 3d multimedia search and retrieval 3d virtual reality / 3d augmented reality 3d interaction and interactive serious gaming design human-computer interaction design medical image processing and visualisation 2d/3d computer vision advanced multimedia design and 3d technologies msc 3d film design and production | ee5565 multimedia and interaction design | ee5557 digital design and branding msc digital media technologies | ee5504 digital design bsc interaction design & usability | ee1706 advanced 3d imaging systems | ee3617
![Dr Mohammad Swash]()
Dr Mohammad Swash
Dr Rafiq Swash joined the Department of Electronic and Computer Engineering, College Of Engineering, Design and Physical Sciences, Brunel University London, UK in 2013. Before that, he held research positions in the area of multimedia search & retrieval systems, expert systems and 3D imaging & display systems at Brunel University London. He received his Ph.D. degree in Holoscopic 3D Imaging Systems: Camera / Processing / Display from Brunel University London, UK. He is a member of IEEE, IET, IEEE Broadcast Technology Society, The Optical Society America (OSA), and The Society for Information Display (SID). In the past, Dr Swash has worked as a senior software engineer, senior technical architect, technical director and chief technology officer in international gaming, financial and robotics industries. Dr Swash has a wide research interests includes 3D imaging and display systems (3D cameras / 3D display / 3D processing), 3D virtual reality / augment reality, 3D computer vision, medical image processing & visualisation, multimedia search & retrieval, human-computer interaction design, interactive game design including serious games and gamification Advanced 3D imaging Systems: holoscopic and multiview 3D displays, holoscopic 3D cameras, 3D image processing 3D image reformatting Advanced 3D computer graphics Multiscopic and stereoscopic 3D systems 3D visual engineering / 3D multimedia search and retrieval 3D virtual reality / 3D augmented reality 3D Interaction and interactive serious gaming design human-computer interaction design Medical image processing and visualisation 2D/3D Computer vision Advanced Multimedia Design and 3D Technologies MSc 3D Film Design and Production | EE5565 Multimedia and Interaction Design | EE5557 Digital Design and Branding MSc Digital Media Technologies | EE5504 Digital Design BSc Interaction Design & Usability | EE1706 Advanced 3D Imaging Systems | EE3617
Taylor
imon j e taylor is a professor of computer science specialising in modelling & simulation and digital infrastructures. he has made many contributions to manufacturing, health care and international development. he has worked with international consortia (in particular unict, wacren and the ubuntunet alliance) to contribute to the development of national research and education networks in africa that has impacted over 3 million students and 300 universities. he has also worked with international consortia (in particular saker solutions, the university of westminster, sztaki and cloudsme ug) to develop high performance simulation systems that are being used by over 30 european smes and large-scale enterprises such as the ford motor company and sellafield plc. he continues to work closely with industry - his work has led to over £30m of savings and new products in industry. he also contributes to the development of open science principles and practice for africa and for modelling & simulation as a field. he has led modules in distributed computing in the department of computer science for many years with high module evaluations scores and is an enthusiastic teacher. he has also led the development of several postgraduate degrees. he has supervised over 20 doctoral students, has examined more than 25 doctoral students from across the world and has managed over 15 research fellows. professor taylor co-founded and is a former editor-in-chief of the journal of simulation and the uk operational research society simulation workshop series. he chaired acm sigsim between 2005-2008 and since then has been an active member of the acm sigsim steering committee. he is also the general chair for the 2025 winter simulation conference. he has chaired international standardisation groups under the simulation interoperability standards organization and has conducted several organisational review panels (e.g., dstl) and simulation audits. he is currently the executive chair for the annual simulation exploration experience ( and a member of the computer simulation archive steering committee ( he has also chaired several conferences and is the general chair for the ieee/acm 2025 winter simulation conference. interested in the history of computer simulation? visit the computer simulation archive hosted by ncsu and hear talks from some of the pioneers in computer simulation. i am strongly interested in modelling & simulation and digital infrastructures, particularly in the development of high performance simulation infrastructures and services in industry and health care. these are extremely important as it allows users to perform more simulation experimentation and to get deeper insight into their problems. this has openned up a new area of study that is allowing us to develop novel ai-based optimisation techniques for modelling & simulation that leverage our high performance simulation infrastructures that we have already deployed in industry (e.g., ford, saker solutions and sellafield). in parallel with these interests i have been able to work towards the development of digital infrastructures and services in africa. this has contributed to the rapid development of african national research and education networks and the foundation for african open science. this work continues and we are working with african stakeholders to further develop african open science and data science approaches across the continent. in turn these experiences have enabled me to contribute to open science techniques for modelling & simulation, as well as open science at brunel. modelling & simulation digital infrastructures and services cloud computing international development open science i teach a variety of subjects from modelling & simulation to distributed computing at undergraduate, postgraduate and national levels (e.g. natcor). i also support student projects and (unpaid) internships in these areas.
![Professor Simon Taylor]()
Professor Simon Taylor
imon J E Taylor is a Professor of Computer Science specialising in Modelling & Simulation and Digital Infrastructures. He has made many contributions to manufacturing, health care and international development. He has worked with international consortia (in particular UNICT, WACREN and the UBUNTUNET ALLIANCE) to contribute to the development of National Research and Education Networks in Africa that has impacted over 3 million students and 300 universities. He has also worked with international consortia (in particular Saker Solutions, the University of Westminster, SZTAKI and CloudSME UG) to develop high performance simulation systems that are being used by over 30 European SMEs and large-scale enterprises such as the Ford Motor Company and Sellafield PLC. He continues to work closely with industry - his work has led to over £30M of savings and new products in industry. He also contributes to the development of Open Science principles and practice for Africa and for Modelling & Simulation as a field. He has led modules in distributed computing in the Department of Computer Science for many years with high module evaluations scores and is an enthusiastic teacher. He has also led the development of several postgraduate degrees. He has supervised over 20 doctoral students, has examined more than 25 doctoral students from across the world and has managed over 15 research fellows. Professor Taylor co-founded and is a former Editor-in-Chief of the Journal of Simulation and the UK Operational Research Society Simulation Workshop Series. He chaired ACM SIGSIM between 2005-2008 and since then has been an active member of the ACM SIGSIM Steering Committee. He is also the General Chair for the 2025 Winter Simulation Conference. He has chaired international standardisation groups under the Simulation Interoperability Standards Organization and has conducted several organisational review panels (e.g., DSTL) and simulation audits. He is currently the executive chair for the annual Simulation Exploration Experience ( and a member of the Computer Simulation Archive steering committee ( He has also chaired several conferences and is the General Chair for the IEEE/ACM 2025 Winter Simulation Conference. Interested in the history of computer simulation? Visit the Computer Simulation Archive hosted by NCSU and hear talks from some of the pioneers in computer simulation. I am strongly interested in Modelling & Simulation and Digital Infrastructures, particularly in the development of high performance simulation infrastructures and services in industry and health care. These are extremely important as it allows users to perform more simulation experimentation and to get deeper insight into their problems. This has openned up a new area of study that is allowing us to develop novel AI-based optimisation techniques for Modelling & Simulation that leverage our high performance simulation infrastructures that we have already deployed in industry (e.g., Ford, Saker Solutions and Sellafield). In parallel with these interests I have been able to work towards the development of digital infrastructures and services in Africa. This has contributed to the rapid development of African National Research and Education Networks and the foundation for African Open Science. This work continues and we are working with African stakeholders to further develop African Open Science and Data Science approaches across the continent. In turn these experiences have enabled me to contribute to Open Science techniques for Modelling & Simulation, as well as Open Science at Brunel. Modelling & Simulation Digital Infrastructures and Services Cloud Computing International Development Open Science I teach a variety of subjects from Modelling & Simulation to Distributed Computing at Undergraduate, Postgraduate and National levels (e.g. NATCOR). I also support student projects and (unpaid) internships in these areas.
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
![Professor Zidong Wang]()
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