Find a PhD Supervisor

Research Opportunities	Supervisors
Distribution network modernization. Electrical energy storage. Grid integration of renewable generation. Power system ancillary services and power system analysis. Dr Best’s research interests are in distributed generation, electrical energy storage, phasor measurement unit applications, smart grids and power system operation with high renewable penetration.	Dr Robert Best
Development of Highly Sensitive Chemical Detection Platform In-situ Characterization of Nanomaterials Internet of Things based Calibrated Low Cost Pollution Monitoring Sensor Network • MEMS based chemical sensors• MEMS based oscillators for mass sensing• Miniaturization of analytical systems• Development of Internet of things based sensor systems	Dr Hamza Shakeel
Efficient hardware accelerators (FPGAs, VLSI) for Lattice based cryptography Efficient and secure cryptographic software for embedded platforms Cryptanalytic vulnerabilities and countermeasures for Post Quantum cryptography Side channel analysis (SCA) attacks for cryptographic hardware I strive to carry out world class research in the area of hardware security in general and post quantum cryptography in particular. Post quantum cryptography is a hot area of research, this class of novel public key cryptography must be researched, analyzed, standardized and adapted world-wide before the advent of quantum computers! My key research interest lies in studying area-efficient, side channel secure, custom hardware implementations of Post quantum cryptography.	Dr Ayesha Khalid
High frequency electronics, antennas and RF-front end My general research interests include (but not limited to): Device physics level: Active/passive antennas at microwave/millimeter waves Metamaterials and transformation electromagnetics Bio-electromagnetism Active, reconfigurable and tuneable systems Computational electromagnetics & multiphysics Higher Level Telecoms: MIMO systems Smart cooperative networks: like body area Physical-layer security schemes	Dr Muhammad Ali Babar Abbasi
Develop and implement multidisciplinary research proposals, particularly in the machine learning. Open to PhD applications in the areas of machine learning and computational modelling Dr. Gault’s primary research is Artificial Intelligence with particular interest in machine learning, and computational modelling of sensory systems. His research is related to medical applications including the development of tinnitus compatible models to improve understanding of the biological basis for tinnitus, the use of computer vision in the detection of stroke symptoms and in cancer research, the modelling of visual encoding by the retina.	Dr Richard Gault
I am looking for UK/EU candidates interested in pursuing a PhD thesis in wireless communications theory, in particular in massive MIMO, and cell-free massive MIMO systems. Dr Hien Ngo is currently a Lecturer at Queen's University Belfast and a UKRI Future Leaders Fellow. His general research interests are the application of mathematical, information theory, and signal processing to wireless. His specific research interests include massive (large-scale) MIMO, cell-free massive MIMO, and cooperative communications. He has received three prestigious prizes: the IEEE ComSoc Stephen O. Rice Prize in 2015, the IEEE ComSoc Leonard G. Abraham Prize in 2017, and the Best PhD award 2018 by the European Association for Signal Processing (EURASIP).	Dr Hien Quoc Ngo
Academic and industry around understanding and evolving software David’s software engineering research resolves around how organisations can understand and evolve their legacy software with as little risk as possible. His educational research includes the use of aptitude testing as a marker for programming skills and providing tools for automated instant feedback to students.	Dr David Cutting
Power Converters for Offshore wind systems and Medium Voltage DC grids Developing Power Converters for power networks and renewable energy systems Developing Power Converters for the next Generation of Electric Vehicles Topics related to Power Electronics, Renewable Energy, and Energy Conversion My research interests span several areas in Power Electronics, including microgrid, distributed energy generation, grid integration, and DC-to-DC converters for Electric Vehicles. I am responsible for developing the Power Electronics research theme, promoting it for undergraduate teaching and postgraduate research within the Energy Power and Intelligent Control EPIC Research Centre. Our research activities include modelling, analysis, design and control of power converters to meet the challenging requirements of high efficiency, compact size, power rating and system integration with the minimum number of components.	Dr Ahmad Elkhateb
Autonomous vehicles The internet of things Satellites Healthcare imaging and sensors Professor Vincent Fusco IEEE Fellow, FREng, FIAE, MRIA, FIET, is Professor of the High Frequency Electronics. He is also CTO of the Institute of Electronics, Communications and Information Technology (ECIT), QUB. He holds the IET most senior award the Mountbatten Medal for his contributions to the UK Microwave Industry. He established the field of active antenna technology for advanced wireless applications, and has made seminal contributions to the fundamental understanding of self-tracking antennas and nonlinear phase conjugating surfaces. His current research focus is on multimode antenna arrays, low cost phased arrays and synthetic electromagnetic materials for Space and for medical sensor applications.	Professor Vincent Fusco
Software engineering including Automated Software Refactoring Requirements Engineering and Evolution Software Evolution prediction Dr Greer’s research is largely about software evolution: how to predict how software will change, how to assess change, how to plan for change, how to manage it and even how to make changes in software for the better automatically. Much of the research can be applied to real problems and the research is often using an experimental approach. Overall, the focus has been on advancing software adaptability in software process and the software product. One productive avenue has been Search Based Software Engineering (SBSE) which attempts to apply search heuristics to solve complex problems in software development. One application of this to the software process is in release planning. Nowadays most software processes are iterative and evolutionary and deciding which requirements go in which incremental release is not always straightforward, due to often conflicting economic considerations, risks, technical constraints and user preferences. SBSE can also be applied to optimizing software code to improve the software product. This is the basis for automated maintenance or refactoring. Making code better i.e. higher quality or easier to maintain or reuse, without intense human effort creates obvious benefits.	Dr Desmond Greer
Power and reliability characterisation and machine learning based modelling of processors and memories Energy-efficient software, hardware design schemes for dependable/secure systems Accelerators (FPGAs, co-processors) in Cloud and Edge environments Cross-layer approximate computing based techniques Circuits and systems for multimedia, communications and machine learning applications The primary focus of Karakonstantis’ research lies on the development of energy-efficient and dependable circuits and systems for portable embedded devices and high-end servers targeting various multimedia, communication, data-mining, health-monitoring, as well as high-performance applications within Edge and Cloud environments. The group’s work spans three main topics: Characterization of the intrinsic variability in memories and design of low cost SW/HW schemes for enabling operation at extended operating points, while addressing potential hardware induced errors. Development of error-resilient, approximate and low-power general-purpose and application-specific processors by exploiting application characteristics. Implementation of data-processing accelerators and scheduling algorithms for utilizing FPGAs and GPUs in Cloud data-centres and Edge environments.	Dr Georgios Karakonstantis
Smart Grid data analytics including Wind/Solar energy/Demand forecasting, Smart Grid Event Detection and Classification Energy storage optimization to facilitate renewable energy integration Dr Amy Liu’s research focuses on ‘smart grid data analytics’ for future energy systems, working closely with industry to address major challenges in the clean energy and energy storage domain.	Dr Xueqin Amy Liu
Machine learning, robotics, planning Dr Vien's research focuses on looking for the bridge among machine learning and artificial intelligence techniques with robot learning and motion planning. His broad research interest lies in three main topics: Intelligent Systems: Reinforcement learning, policy search, deep reinforcement learning, planning under uncertainty (POMDPs), learning from human feedback; Machine Learning: Probabilistic modeling, deep learning, Bayesian inference, active learning; Robotics: Motor skill learning, movement primitives, manipulation under uncertainty, model learning, motion planning, optimal control, and human-robot interaction	Dr Vien Ngo
Wireless communications Massive MIMO or fog computing Dr Matthaiou’s research interests span signal processing for wireless communications, massive MIMO, hardware-constrained communications, and fog computing. He has published some 150 papers on these topics. Dr. Matthaiou and his coauthors received the IEEE Communications Society Leonard G. Abraham Prize in 2017. He was the recipient of the 2011 IEEE ComSoc Best Young Researcher Award for the Europe, Middle East and Africa Region and a co-recipient of the Best Paper Award at the 2014 IEEE International Conference on Communications (ICC).	Dr Michalis Matthaiou
SDNFV security Predictive analytics for network security Network monitoring and forensics Edge-based network security Performance-optimized security implementation We are focussed on research and development of performance-optimized network security architectures and functions for Software-Defined Networks (SDN) and Network Functions Virtualization (NFV). With networks moving towards a software-defined approach and increasingly introducing virtualization technologies, we contribute by identifying, raising awareness and recommending solutions to potential vulnerabilities in SDNFV network design and deployment. In parallel, we are exploring scalable, analytics-based monitoring and forensics capabilities, and security solutions for these new network architectures.	Dr Sandra Scott-Hayward
High-performance computing, runtime systems and compilation, Performance and scalability aspects of data analytics, in particular graph analytics. Hans’ research seeks to understand how to maximise the utilisation of computer systems in order to increase speed of computation, or reduce energy consumption. A central tenet of his research is to tease out key characteristics of applications, software and hardware and leverage these to develop bespoke algorithms and optimizations. His research focusses on runtime-system techniques, compilation and application-specific solutions. He has applied his research to scientific computing codes and data analytics problems.	Dr Hans Vandierendonck
Simulation of musical instruments, audio circuitry and audio processing systems Design and development of sensor interfaces for virtual-acoustic instruments Interactive sounding objects in VR/AR/MR environments Study of musician-instrument interaction and participatory design strategies Interactive Virtual-Acoustic Spaces Dr Van Wakstijn's research currently focuses on Numerical methods for simulation of mechano-acoustic systems and audio circuitry incorporating musically relevant non-linear phenomena Design and formulation of versatile, computationally robust, efficient and fully tunable algorithms Code optimisation for real-time audio rendering on standard and embedded systems Design and development of sensor-based control interfaces that support nuanced performance affordances Incorporation of instruments and other interactive sounding objects in VR/AR/MR environments Study of musician-instrument interaction and participatory design strategies	Dr Maarten Van Walstijn
New Edge Computing Solutions for Machine Learning Physical Layer Security Approaches for Internet-of-Things Advanced Data Analysis for Manufacturing Applications With the expected demise of Moore’s Law, there is a renewed challenge to explore computer architecture and lower power technologies such as FPGAs in the design of new, more powerful embedded systems. This has major implications for the design of autonomous systems, embedded AI systems and wireless communications. Roger’s research is involved in the design and implementation of innovative systems for image processing systems, security systems for WiFi-based networks and embedded AI platforms for data analytics. There is as focus on practical implication and close collaboration with industry.	Professor Roger Woods
mm-wave antennas and systems Wideband millimetre wave imaging antenna Automotive radar sensor antenna system Dr Zelenchuk's main research interests are: Antennas for ultra-compact mm-wave automotive radar Mm-wave material characterisation methods Integrated antennas mm-wave communication links Novel mm-wave waveguiding structures and antennas Complex beamforming antennas for 5G communications Polarisation and frequency controlling surfaces Metamaterials	Dr Dmitry Zelenchuk
Machine Learning in Precision Medicine Computational Methods for Online Fake News Detection Machine Learning for Heterogeneous Data Analysis Anna’s research interest span several sub-fields of data science including theoretical and applied machine learning, data mining and natural language processing. In particular she is interested in three topics: (1) Application of machine learning in biology and medicine; (2) Creating automated solutions for online fake news detection and verification; (3) Integration and analysis of heterogeneous data (entity resolution, record linkage).	Dr Anna Jurek-Lougherey
KTP and FUSION projects EPSRC projects H2020 projects The core of my work is building high performance computing software systems to solve numerical problems. This can be on small embedded board computers at one extreme on supercomputers with hundreds of thousands of nodes. It includes using heterogeneous accelerators on these systems: FPGAs and GPUs. This work involves collaboration with colleagues in several problem domains including: atomic and molecular scattering physics, electromagnetic fields, image analysis and clinical computational physiology. For the past few years I have worked extensively with colleagues in the QUB Medical School to develop data analytics and machine learning systems that process streams of physiological parameters from the bedside of patient in an intensive care unit.	Dr Charles James Gillan
Physical Unclonable Functions (PUFs) Approximate computing for hardware security Hardware security techniques for the Internet of Things (IoT) Machine learning attack resistant hardware security solutions All cryptographic algorithm based security primitives and protocols depend on hardware implementation to achieve the expected protections even though cost billions of dollars on software-only malware detection, virus scanners, firewalls and much more. The recent Meltdown and Spectre vulnerabilities on processors demonstrated a good example of hardware based attacks. The hardware security threats have spread every corner of the semiconductor supply chain. Moreover, Cisco reported that there would be 500 billion connected devices by 2030, which means the Internet of Things (IoT) will revolutionise our lives. However, the vulnerabilities of IoT devices also bring challenges to our lives. Her research is to bring trust to lightweight IoT devices with cutting-edge hardware security technologies.	Dr Chongyan Gu
Fake News Detection Fairness and Ethics in Machine Learning Spatio-Temporal Data Analytics Deepak’s research is centered on data analytics spanning areas such as natural language processing and spatio-temporal data processing, with a focus on unsupervised tasks within those realms. His current interests span three distinct streams: Fairness and Ethics in machine learning, with a specific focus on unsupervised machine learning (e.g., clustering, retrieval, anomaly detection). Data science approaches for fake news detection, with a focus on predominantly textual manifestation of fake news such as online articles (health and science domains, for example) as well as graph-based methods. Spatio-temporal data processing: Mining datasets where location information is central, in order for various applications such as game analytics, retail loyalty and location-based social networks.	Deepak Padmanabhan
Mobile apps and Fog computing Automated engineering of mobile apps (e.g., app design, maintenance, review analysis) Design principles/patterns and micro-services Web APIs and mobile apps Distributed frameworks for direct democracy Dionysios’s research mainly focuses on developing automated (design and run-time) engineering techniques for service-oriented software Web APIs mobile apps (e.g., mobile back-end as a service). These techniques are based on applying design principles/patterns employing data-engineering (e.g., data mining, matching, integration) techniques developing self-adaptive mechanisms. Last but not least, he has recently proposed a distributed game-theoretic framework for direct democracy (applying software engineering in society).	Dionysios Athanasopoulos
Cyber security for Industrial Control Systems (ICS), smart grid and SCADA networks Network forensics for smart grid and ICS Secure cloud platforms for smart grid and ICS Information Centric Networking (ICN): cyber security aspects and application to ICS Network intrusion detection Network security and cyber security for operational technologies (OT). This includes smart grids, industrial control systems (ICS), supervisory control and data acquisition (SCADA), cyber-physical systems, and related critical infrastructure networks. Research interests include threat analysis, intrusion detection and prevention, intrusion response, cyber-physical resilience measures, and security of network protocols such as IEC61850 and IEC60870.	Dr Kieran McLaughlin
Practical post-quantum cryptography IoT Device Authentication - Physical Unclonable Functions (PUFs) Side Channel Analysis (SCA) Cryptographic Hardware & Software Architectures Hardware Security and applied cryptography.	Professor Maire O'Neill
Deep learning for automated visual inspection Intelligent systems techniques for improving manufacturing competiveness Evolving Intelligent Behaviour in Cooperating Robots Flexible automation and cobotics Prof McLoone’s research interests are in the general area of intelligent systems with a particular focus on data based modelling and analysis of dynamical systems. This encompasses techniques ranging from classical system identification, fault diagnosis and statistical process control to modern artificial intelligence and biologically inspired adaptive learning algorithms and optimization techniques. His research has a strong application focus, with many projects undertaken in collaboration with industry in areas such as process monitoring, control and optimization, time series prediction and in-line sensor characterization. Current applications are in advanced manufacturing informatics, energy and sustainability.	Professor Seán McLoone
Wireless Sensing and Internet of Things Intelligent Metasurface Panels and/or Wireless Power Transfer and RF Energy Harvesting My main research interests lie in both the RF Engineering and Wireless Communications scientific areas. Specifically, I am working on the area of Wireless Sensing and Internet of Things (IoT), Intelligent Electromagnetic Metasurfaces and RF Energy Harvesting. I have published in excess of 40 papers on these topics, including some 18 Nature/IEEE journal papers.	Dr Stylianos Assimonis
Data Mining Machine Learning High Performance Computing Data Mining and Machine Learning applications in neuroscience, medicine, public health, transportation, etc. Dr Mai‘s main research focuses on high performance machine learning and data mining techniques for finding meaningful patterns from big complex data using modern hardware architectures such as multicore CPUs and GPUs. Besides that, he has worked on several other research topics in CS such as computer graphic, computational geometry, optimization techniques, local searches and metaheuristic searches, time series analysis, and constraint satisfactory problems. He is also interested in machine learning and data mining applications in other fields such as neuroscience, medicine, transportation, and public health.	Dr Son T Mai
Homomorphic encryption Practical Post-quantum cryptography Cryptographic hardware architectures Functional encryption (identity-based encryption, attribute-based encryption) My research interests lie in the area of applied cryptography and hardware design. I currently am working on research projects in post-quantum cryptography and homomorphic encryption.	Dr Ciara Rafferty
Antenna design for difficult and challenging environments EM wave propagation analysis Experimental measurements for wearable communication Human tissue phantoms and test-beds Wireless communication for medical sensors Ultra-thin artificial electromagnetic materials I have built an established programme of research activity in the areas of RF and microwave devices and wireless communications. Much of this effort has targeted the provision of wireless connectivity for medical healthcare applications with the goal of engaging and impacting people’s lives and making a positive contribution to society. My research interests encompass antennas, human tissue materials, printable microwave advanced surfaces and materials, wireless medical sensors, wave propagation and electromagnetism for wearable and Implantable communications.	Dr Gareth Conway
Applied Machine learning and Natural Language Processing in Health Analytics Model interpretation in NLP and interdisciplinary research at the intersection of NLP and Cognitive Science Lexical semantics and semantics in computer vision Dr Devereux has research expertise in large-scale data analytics, cognitive science, machine learning, and natural language processing (NLP). His research takes an interdisciplinary approach to investigating linguistic and visual data, combining artificial intelligence and machine learning methodologies with information derived from large-scale text and image resources, and applying the resulting computational models to multivariate statistical analysis of human behaviour and neuroimaging data. He is also interested in the application of machine learning and NLP in healthcare and bioinformatics.	Dr Barry Devereux
AI for security Computer vision Cybersecurity and data analytics Deep learning Jesus has deep expertise in machine learning, deep learning, computer vision and data analytics, with application into cyber and physical security. His research interests include Deep Learning for cybersecurity, Human motion analysis, Pose estimation, Activity recognition and Multi-target tracking in real time.	Dr Jesus Martinez del Rincon
Software testing Empirical software engineering Search-based software engineering Model-driven engineering Evidence-based software engineering My research expertise in software engineering include: software testing, empirical software engineering, search-based software engineering, model-driven engineering, and evidence-based software engineering. Furthermore, I take a strong interest in conducting industry-academia collaborative research and agile methodologies. In the last 15 years, I have collaborated with 40+ software companies as a PI (principal investigator), mostly in Canada and Turkey. My students and I have helped our industrial partners develop and test software-intensive systems in an effective and efficient manner. We have published many papers as outcomes of those projects.	Dr Vahid Garousi
Custom hardware and high-level synthesis for FPGA Edge computing and cyber-physical systems Signal processing systems Dataflow computing Neuromorphic computing and spiking neural networks John’s research addresses the design of custom hardware for embedded/edge signal processing and machine learning applications – particularly Field Programmable Gate Array (FPGA). It sits at the intersection of advanced algorithm, compiler/synthesis technology and custom hardware architectures. He is particularly interested in novel computational paradigms, including dataflow, neuromorphic and quantum computing.	Dr John McAllister
Smart Grid Telecommunications Phasor Measurement Units Electrical Power System Instrumentation Cyber Security of Critical Infrastructure Distribution Network Monitoring and Real-Time Control Smart Grids are electrical power networks which use measurement and telecommunications to improve energy efficiency and promote renewable generation. David’s work encompasses open source measurement technology, cyber-secure telecommunications, and data management, processing and algorithms. David is the founder of the open source Phasor Measurement Unit (OpenPMU). Present research interests include PMUs and their applications, software defined networks (SDN) in IEC 61850 substations, and precision time transfer (including GNSS / GPS / PTP).	Dr David Laverty
Robotics and human-robot interaction/collaboration Process control for manufacturing systems Sensing and perception for robotics and manufacturing systems Dr. Van’s research focuses on two key research areas: robotics and control engineering. In robotics, the research work focuses on building fundamental capabilities to address practical problems that limit the use and potential impact of robotics in industry, i.e., welding robot, finishing robot, human-robot interaction. In control engineering, the research work focuses on robust control, model predictive control, shared control and adaptive control. The control theories are applicable for robotics, manufacturing systems and marine engineering: launch and recovery systems.	Dr Mien Van
Self-steered antennas for wireless power transfer Novel Injection Locked Transceivers for RFID and Healthcare Practical solutions for frequency diverse arrays Dr Buchanan’s main research interests are: Antenna Arrays Self steered antennas for wireless power transfer Antennas for space applications eg. Satellite ground terminals, Launch Vehicles Novel sensors for Healthcare and RFID High Efficiency, Non-linear, High Frequency Circuits	Dr Neil Buchanan
Cyber-physical systems control: advanced manufacturing, robotics, resource allocation, networked systems, power electronics, edge computing Hybrid systems / switching systems analysis and control Reachability analysis and set-based methods Systems subject to communication, computation and physical constraints Systems and Control theory, especially in hybrid systems using set-based methods, with emphasis on applications involving communication, computation and physical constraints.	Dr Nikolaos Athanasopoulos
Antennas and metasurfaces with a strong focus on microwave and millimeter-wave imaging and computational radar systems Applied electromagnetics, antennas and propagation, metamaterials and metasurfaces, microwave and millimeter-wave imaging, computational imaging, compressive sensing and radar systems.	Dr Okan Yurduseven
Microwave imaging for biomedical and security applications Environmental sensing MIMO communications Underwater sensing Nano- and biomaterials characterization Electro-acoustics and sound engineering Microwave imaging for biomedical and security applications. Microwave screening for early stage cancer detection, environmental sensing, nanomaterial-based sensing, electronic sound engineering. MIMO communications.	Dr Oleksandr Malyuskin
Robust speech recognition with unseen/unpredictable noise Speech enhancement & separation with untrained noise and crosstalk interferences Robust speaker and audio source recognition Speech, audio and language processing, for speech / audio recognition, enhancement and separation, with a focus on robustness against unseen/unpredictable noise and limited training data.	Professor Ji Ming
ChemFET-based biological & environmental sensors Nanostructured plasmonic arrays for biosensing applications MEMS technology & devices Dr McNeill has worked on a wide variety of microelectronics and MEMS research. In recent years he has concentrated on sensor design and fabrication. A current project is exploring silicon-based ChemFET technology for biological sensing and a recent US/Ireland project looked at 2-dimensional semiconductors which could offer improved sensitivity. The Nanoscribe instrument in the QAMEC facility allows low-cost nanostructures to be produced and it is hoped to use these for sensor devices.	Dr David McNeill
None at present Computer graphics, Virtual reality, numerical modelling.	Dr Stuart Ferguson
Machine learning and Computer Vision Fundamental research topics and real-world applications in computer vision and machine learning, specifically, Visual recognition and video understanding Visual learning with limited labeled data Robust representative learning and its interpretations - Hardware-aware machine learning	Dr Yang Hua
Virtual Reality Mixed Reality Personalised Interaction through Machine Intelligence Multi-sensory interaction The focus of my research is the application of tools and technologies to lead new disruptive practices and systems for many application areas. The main application areas I work with are Health & Training and Industry & Automation. I am particularly interested in how virtual and augmented reality technologies can be used in these areas and how they can change the future practices within such areas. I have over fifteen years’ experience working within the fields of software engineering, sensor fusion and real time software development and I have over ten years’ experience working within the areas of virtual and augmented reality and multi sensorial systems.	Professor Karen Rafferty
Data analytics and machine learning in biological context GeneRank: A machine learning approach to detect and rank gene signatures from biological repositories with an application in lung cancer Generally speaking, my research focus on developing AI & machine learning algorithms/software/tools in biological context specifically cancer-associated diseases including high-dimensional datasets/cohorts. My main research aim is to develop intelligent algorithms for identifying key biomarkers in various cancer subtypes and ultimately improve patients' treatment in the clinical settings.	Dr Reza Rafiee
Deep Learning Machine Learning Cyber-Security Malware Detection Computer Vision Deep learning and machine learning applied to cyber-security and physical security. This may include malware detection and analysis, as well as video and image analysis for multi-person tracking, pose estimation and re-identification.	Dr Niall McLaughlin
Path planning and collision avoidance for mobile robots Collaborative robotics and cooperative control of dual arm manipulator systems Dr Naeem has significant expertise in the development of advanced navigation, guidance and control algorithms for unmanned mobile vehicles. His current research is focused on developing collision avoidance, path planning, formation control systems and simultaneous localisation and mapping (SLAM) algorithms for marine, land and aerial vehicles. Additionally, cooperative control of dual arm systems such as a humanoid in the context of advanced manufacturing is an active area of research.	Dr Wasif Naeem
Energy preservation and protocols Monitoring PM2.5 and NO2 (Air Quality) Resource management and Optimization in Wireless Communications. Energy optimization. Energy Harvesting. Performance assessment of Future High Dense networks. Wireless Sensor Networks and IoT. IoT solutions. Cognitive Radio networks. Spectrum management. Interdisciplinary Research: Privacy and Trust in Smart Environments and IoT Sensing and Forecasting PM2.5, NO2 (Air Quality)	Dr Emi Garcia-Palacios
Wireless communications Realtime Optimisation Machine Learning Data Analytics 1) Wireless communications: physical layer security, UAV communications, URLLC, robotic communications 2) Realtime optimisation, Machine learning, Big data analytics and data science 3) Their applications to environment, agriculture, healthcare, plastic waste, policy making, and smart cities	Dr Trung Q. Duong
Heterogeneous and cloud computing Machine learning on heterogeneous systems Distributed and cloud computing, implementation techniques for improving energy and time performance on heterogeneous system. Machine learning on heterogeneous systems. Benchmarks and solvers for the satisfiability and model counting problems.	Dr Ivor Spence
Foundation of machine learning: contextual probability inspired statistical modelling - a deep learning approach, and its application e.g. digital twins. Machine learning: detection learning that aims to learn signatures of objects of interest, and its applications in food authentication, virus detection, medical image analysis (disease imaging biomarker). Knowledge representation and reasoning: ontological competency modelling and automated competency-based assessment. My research interests are machine learning, knowledge representation and reasoning, and their applications in image, video, spectra and text data analyses. Specific research outputs include lattice machine (an algebraic framework for machine learning by generalisation and knowledge representation), contextual probability (a perceptionist formulation of probability), and neighbourhood counting (a generic similarity measure that can be specialized to any form of data including multivariate data, sequences, tree and graph structures). My current focus is detection learning and knowledge-based learning. Machine learning, knowledge representation and reasoning, computer vision	Dr Hui Wang
Machine learning models in healthcare applications Explainable deep learning models Next-generation activity recognition in smart homes A proactive approach to understand the healthcare data and predict the outcome The role of machine learning in Digital Twins My research focus is to design and develop cutting-edge machine learning models to solve the challenges of bias in data-driven approaches, healthcare data analysis, and interpretations of model outcomes. I am very much interested in connecting industry and academia by developing machine learning-based solutions. Machine learning, Healthcare data analysis, Data driven models, Wearable computing	Dr Muhammad Fahim
Malware analysis and detection engineering Digital Forensics Mobile systems privacy and security Privacy-preserving systems (Requirement, Specification, Protocols, Engineering, Awareness, Verification, Regulatory Compliance, Sensitive Information-flow analysis etc.) The majority of my current research revolves around understanding the complexities of secure cyberspace that necessitates evolving, dynamic and sustainable solutions to the issues and challenges. Attacks and Defences; Software and Platform Security; Human, Organisational and Regulatory Aspects knowledge areas of the Cyber Security Body of Knowledge (CyBOK - https://www.cybok.org/) are all areas of research that I am interested in. My current research focus includes mobile systems security, privacy-preserving systems, digital investigation and forensic data processing, sustainable malware analysis and detection engineering. Malware, Adversarial; Forensics; Mobile Security; Cybersecurity; Privacy; Security; Information Security; Secure Software, Privacy Laws	Dr Oluwafemi Olukoya
Embedded numerical optimisation Model predictive control of uncertain systems Learning-based and data-driven control Massively parallelisable algorithms for large-scale optimisation problems and applications of the above in emerging and future technologies such as; Safe context-aware collaborative robotics, Energy distribution management in microgrids with high penetration of renewables Autonomous ground and aerial vehicles (co-existence of driverless and conventional vehicles) Advanced manufacturing My research focuses on the development of efficient numerical optimisation algorithms that can run on embedded devices and parallelised on GPUs and the development of model predictive control methodologies for uncertain systems with emphasis on autonomous vehicles, smart infrastructure networks, same and optimal drug administration, and advanced intelligent manufacturing. numerical optimisation, model predictive control, autonomous vehicles, collaborative robotics, pharmacokinetics, drug administration, manufacturing, robotics, control	Dr Pantelis Sopasakis

Microarchitecture level :

Novel solutions to enforce security inside a chip
Multiprocessor Systems-on-Chip
Network-on-Chip
Embedded system security using microarchitectural solutions

Software level :

Static analysis based vulnerability detection
OS and compiler level security solutions

Dr Arnab Kumar Biswas

CPS-IoT Behavior Modeling and Beyond
UAV Communications and Security
5G and Beyond Security
Blockchain Systems

Dr Vishal Sharma

Electronics, Electrical Engineering and Computer Science

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