Group Members

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

Vice-Dean of the NCUT TNE programme/Professor

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)

Dr Takebumi Itagaki

Honorary Senior Lecturer

Dr Takebumi ITAGAKI obtained a BEng from Waseda University (Japan) and a PhD in Engineering/Music from University of Durham (UK) in 1998. In 2000, he moved to Brunel University London as a Lecturer in Engineering. He contributed towards the several EU-IST FP5/FP6 research projects, including the SAVANT Project as the prime contractor and administrative coordinator, and the INSTINCT Project as the project manager. He was coordinating the EU CIP PSP Project DTV4All. His expertise include: Digital TV system (DVB, ISDB), Digital Signal Processing, Parallel Processing, Computer Music and Computer Architecture. Currently, he is one of the coordinators of ITU-T Focus Group Audio Visual Accessibility – Working Group D. Multimedia systems, digital signal processing, audio signal processing, Digital TV with Multimedia, IoT with Communciation applications EE2601 (Brunel) EE2623 (CQUPT) Computer Architecture and Interfacing EE3099/EE3600 Final Year Project (Brunel, CQUPT) EE5612 Communication Network Security (Brunel, to be delivered at Ahlia, Bharain) EE5500 Dissertation (Brunel, to be delivered at Ahlia, Bharain)

Dr Harry Agius

Senior Lecturer in Computing

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.

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 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)