Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Wireless Sensor Networks
• +6 more

Antonio Liotta is the Director of the Data Science Research Centre at the College of Science and Engineering . His work bridges Machine Learning , Artificial Intelligence , and Network Science , with applications in Wireless Sensor Networks , Video Quality Assessment , and Environmental Sensing . Research Focus : Analysis of missing data in IoT and criminal networks Optimization of wireless sensor networks for energy efficiency AI-driven quality assessment for video streaming services Urban mobility patterns during pandemics Integration of smart city technologies Publications : Over 15 recent articles in high-impact journals like Sensors and PLoS Collaborations with researchers in Italy, Greece, and the Netherlands Technical Contributions : Decentralized anomaly detection in sensor systems Graph-theoretical models for criminal network simulation Adaptive power control algorithms for wireless networks

Ala Al KafriView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Medical Imaging Analysis
• +5 more

Ala Al Kafri is a Senior Lecturer in Computer Science at Teesside University's Department of Computing & Games, affiliated with the Centre for Digital Innovation. With over 16 years of teaching experience across multiple institutions globally, he specializes in machine learning, artificial intelligence, and medical imaging applications. His research focuses on AI-driven diagnostics for lumbar spine diseases, with notable contributions to MRI analysis using convolutional neural networks. He has collaborated on projects involving physiotherapy education technology, autism screening, and multidisciplinary learning initiatives. Education includes a PhD in Machine Learning for Lumbar Spine Disease Diagnosis from Liverpool John Moores University (2019), an MSc from New York Institute of Technology (2005), and a bachelor’s degree. Professional development includes a Postgraduate Certificate in Academic Practice. Research interests span medical AI, data science, and education technology, with emphasis on practical applications in healthcare and learning environments. Key collaborations include developing an AI tool for lumbar spine disease prediction with University Multimedia Nusantara, and creating video-based physiotherapy assessment systems. Recent projects include AI-powered physiotherapy education systems, autism screening using ML, and smart highway vehicle detection. His work integrates technical innovation with real-world societal impact through multidisciplinary approaches.

• Antimicrobial Resistance
• Molecular Biology
• Infectious Diseases
• +5 more

Dr Abu-Bakr Abu-Median is a Lecturer in Biomedical Science at De Montfort University's School of Allied Health Sciences since 2017. A veterinarian and molecular microbiologist with extensive experience in microbial diagnostics and pathogen research, he has contributed to developing portable molecular diagnostic devices for clinical and field applications. BVSc, University of Khartoum MSc in Applied Molecular Microbiology, University of Nottingham PhD in Molecular Bacteriology, University of Bristol His research focuses span antimicrobial resistance , molecular diagnostics , zoonotic diseases , and microarray technology . He has pioneered DNA-based assays and microarrays for multi-pathogen detection, particularly in rodent and avian species. Recent publications highlight his work in pathogen discovery , viral characterization , and computational homology modeling for microbial virulence factors. His expertise in lab-on-a-chip systems and bioinformatics has advanced wildlife disease surveillance methodologies. Associate Fellow, Higher Education Academy As an educator, he teaches molecular biology and infectious diseases to biomedical science students and supervises PhD candidates. His professional work includes collaborations with The Pirbright Institute and EU/BBSRC funded research projects.

• Machine Learning
• Healthcare Analytics
• Telecommunication Industry Analysis
• +3 more

Karl Hall is a Lecturer in Computing & Games at the Department of Computing & Games, Teesside University. His research focuses on machine learning applications across diverse domains including healthcare analytics, telecommunication, traffic modeling, and social network analysis. He specializes in developing predictive models for credit risk assessment, disease diagnosis, customer behavior analysis, and infrastructure optimization. Key research interests include machine learning algorithms (e.g., Random Forest, CNNs), explainable AI systems, and their practical implementations in real-world scenarios. He has conducted studies on diabetes prediction, pandemic forecasting, and metaverse risk assessment. His work often bridges theoretical models with practical industry challenges, particularly in finance, healthcare, and transportation sectors. Recent research trends show a focus on interdisciplinary applications of AI, such as biomedical information retrieval frameworks and transformer-based models. He has collaborated on projects analyzing UK traffic datasets and Bitcoin trader networks, highlighting his versatility in data-driven research. No awards or grants are explicitly mentioned in the provided texts. His advising and professional activities are not detailed in the current data, but his publications reflect active engagement with both academic and applied research challenges.

Peter OrmosiView profile

Professor

• Competition Economics
• Digital Markets
• Econometrics
• +7 more

Professor Peter Ormosi is a Professor of Competition Economics at the Norwich Business School, University of East Anglia. He is also a member of the Centre for Competition Policy. His research focuses on competition law and economics, digital markets, and the application of machine learning to social sciences. Current projects include UKRI-funded work on trustworthy autonomous recommender systems in music streaming. Ormosi holds PhDs in Economics and Law, with publications in top-tier journals across both disciplines. His research interests span competition policy evaluation, antitrust enforcement impacts, and the role of private sanctions in market regulation. He has advised major institutions including the European Commission, OECD, and UK Competition and Markets Authority (CMA). Teaching responsibilities include undergraduate courses on Economics for Business and MBA modules on the International Economic Environment. His work contributes to UN Sustainable Development Goals, particularly addressing inequality and sustainable economic growth. Recent projects include a study on the impact of retail petroleum market concentration and analysis of supplier competition on digital platforms. He leads a multidisciplinary team exploring the economic implications of autonomous recommendation systems.

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +4 more

Dr. Xinqi Fan is a Lecturer in Artificial Intelligence at Manchester Metropolitan University, with expertise in deep learning, computer vision, and their applications across facial analysis, medical imaging, and energy systems. He has collaborated with institutions including Northern Care Alliance NHS Foundation Trust and the Chinese University of Hong Kong. Ph.D. from City University of Hong Kong M.Sc. from University of Western Australia B.Sc. from Southwest University His research focuses on advancing AI techniques for emotion understanding, interpretable biomarker development, and energy system optimization. Recent work emphasizes multimodal learning, motion retargeting, and spatiotemporal analysis. Publications span leading venues like CVPR, MICCAI, and IEEE TIP. Key trends in his articles include attention mechanisms, graph neural networks, and self-supervised learning models applied to medical diagnostics, facial expression recognition, and renewable energy. Awards include two Silver Medals and academic scholarships. National Scholarship Yuhui Scholarship Geneva Inventions Silver Medal Internet+ Innovation Competition Silver Medal ACM Multimedia Micro-Expression Challenge Runner-up Dr. Fan supervises PhD students in the Human-Centred Computing Group and contributes to academic service as a conference reviewer and former IEEE student branch chair.

Katie Paxton-FearView profile

Lecturer

• Cyber Security
• Data Science
• Artificial Intelligence (AI)/Machine Learning (ML)
• +4 more

Dr. Katie Paxton-Fear is a Lecturer in Cyber Security at Manchester Metropolitan University, specializing in the intersection of data science, AI, and security. Her research focuses on applying data science and AI to cybersecurity challenges while addressing security concerns within AI/ML systems. She is an active web security researcher and educator, contributing free resources via YouTube. Her work includes vulnerability discovery in organizations like Verizon and the Department of Defense, and she explores ancient language decipherment as a side interest. Paxton-Fear holds a PhD in Defence and Security from Cranfield University and a BSc in Computer Science from Salford University. She teaches undergraduate and postgraduate courses on Security Fundamentals, Penetration Testing, and IoT Security. Her research interests span interdisciplinary topics such as secure development practices, API/web security for cyber-physical systems, and leveraging NLP for insider threat analysis. She advocates for collaboration between developers and security professionals, emphasizing proactive security measures. Paxton-Fear is also a media commentator featured in outlets like BBC, ZDNet, and Wall Street Journal, discussing bug bounty programs and diversity in cybersecurity. Her recent publications highlight forward-looking cybersecurity frameworks, prototyping for secure systems, and computational methods for ancient language analysis. She emphasizes data-driven approaches in security research and promotes accessibility to advanced tools like NLP for defense applications. Beyond academia, she actively engages in bug bounty hunting and maintains a YouTube channel dedicated to demystifying cybersecurity concepts for beginners.

Salma IbrahimView profile

Professor

• Accounting
• Financial Reporting
• Corporate Governance
• +4 more

Professor Salma Ibrahim holds the position of Professor and Departmental Research Director at the Department of Accounting, Finance and Informatics within the Faculty of Business and Social Sciences at Kingston University. She is an advocate of research-informed teaching and currently teaches Market-based Accounting, Financial Reporting, and Research Methods at the postgraduate level. Her research focuses on earnings management, governance, and financial reporting in capital markets, and she supervises PhD students in these areas. Professor Ibrahim established the 'Accountability, Governance and the Capital Market Research Hub' in 2016 and has served as its director since its inception. She also serves as an Associate Editor for the Journal of Applied Accounting Research and Journal of Financial Reporting and Accounting , and is a committee member of the EAA Publications Committee until 2026. Her academic qualifications include a PhD in Accounting from the University of Maryland, a Master's in Accounting from Syracuse University, and a Bachelor's in Business from Helwan University. She is a Fellow of the Higher Education Academy (HEA) in the UK. Her professional practice emphasizes integrating non-financial performance measures into executive compensation contracts and exploring the legal consequences of earnings manipulation. Her research interests span earnings management detection methodologies, corporate governance structures, and the intersection of accounting practices with regulatory frameworks. She has contributed to debates on CEO pay transparency, risk disclosure practices, and the impact of ownership structures on financial reporting quality.

Sepehr AbrishamiView profile

Senior Lecturer

• Building Information Modeling (BIM)
• Civil Engineering
• Artificial Intelligence
• +4 more

Dr. Sepehr Abrishami is a Senior Lecturer and Course Leader for MSc BIM at the University of Portsmouth's Faculty of Technology, within the School of Civil Engineering and Surveying. He is a registered architect with extensive experience in Europe and the Middle East, specializing in Architecture, Integrated & Digital Design, Construction Automation, AI, Blockchain, and Virtual Reality applications in construction. His expertise includes BIM integration, offsite manufacturing, and smart building technologies. Education: PhD in Environmental Design (Cardiff University) MSc in Environmental Design First-Class Registered Architect (Iran) Research Interests: Focuses on advancing BIM technologies, blockchain applications in construction, AI-driven design automation, and sustainable construction practices. His work bridges architectural design, digital innovation, and construction management to enhance efficiency and sustainability. Advising & Grants: Supervised multiple PhD/postgraduate students and contributed to projects exploring smart contracts, DfMA, and disaster management using BIM. Active in collaborative research with industry partners on topics like cost optimization and IoT integration. Labs & Teams: Affiliated with the Portsmouth AI and Data Science Centre and the Agile Centre For Equitable Sustainability, advancing interdisciplinary research in AI, sustainability, and construction innovation.

Becky StrachanView profile

Professor

• Games for Learning
• Digital Technologies
• Student Engagement and Partnership
• +1 more

Professor Becky Strachan is a faculty member at Northumbria University's Department of Computer and Information Sciences, with a research focus spanning educational technology, STEM diversity initiatives, and academic integrity challenges. She actively supervises PhD students and has contributed to 72 research outputs and multiple funded projects. Education: PhD in Computer Studies (1991) MSc in Computer Studies (1986) BSc in Chemistry (1984) Advanced Diplomas in Research Methods and Education (1999, 1997) Her research interests emphasize: Developing educational games across disciplines Evaluating digital technologies from end-user perspectives Promoting active learning through student engagement Addressing diversity gaps in STEM fields Recent publications demonstrate interdisciplinary work in computing education, AI ethics, and environmental monitoring applications. Key themes include academic integrity in digital eras, STEM intervention impacts, and global education challenges. Active collaborations include partnerships with Tomas Bata University in Zlin and contributions to national education policy through projects like BRIDGE (Building Routes Into Degrees with Greater Equality). She has secured funding from organizations such as the Higher Education Funding Council for England and Arts & Humanities Research Council.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Prof Sunil Vadera is a Professor of Computer Science at the University of Salford, affiliated with the School of Science, Engineering & Environment. He holds fellowships from the British Computer Society (FBCS) and is a Chartered Engineer (CEng) and Chartered IT Professional (CITP). His leadership roles include former Dean of the School of Computing, Science and Engineering, and Director of the Informatics Research Institute. He received the UK BDO Best Indian Scientist and Engineer award in 2014 and the Amity Award for AI contributions in 2018. His research focuses on bridging theory and practice in AI, with projects like the GM AI Foundry supporting SMEs, EU-funded energy-efficient building systems, and smart meter data analysis for British Gas. His work spans AI applications in healthcare, cybersecurity, IoT, and energy systems. He leads the Deep Learning module in the MSc in AI program and has supervised multiple PhD students, including Dr Salem Ameen. Key research interests include cost-sensitive machine learning, explainable AI, phishing detection, and medical imaging. Recent publications address AI-driven solutions in healthcare (e.g., breast arterial calcification detection), cybersecurity (Arabic phishing emails), and livestock behavior analysis using vision transformers. He has pioneered algorithms like CSNL and EECSDT, advancing decision tree and Bayesian network methodologies. Prof Vadera’s contributions extend to academic leadership, including chairing the BCS Accreditations Committee. His interdisciplinary projects often involve collaborations with industry and government, emphasizing real-world impact. The Informatics Research Institute under his direction fosters innovative AI applications across sectors.

• Medical Imaging
• Electrical Impedance Tomography (EIT)
• Neonatal Lung Monitoring
• +3 more

Dr. Nima Seifnaraghi is a Research Fellow in Medical Imaging at Middlesex University, affiliated with the Natural Sciences faculty. His work focuses on advancing electrical impedance tomography (EIT) techniques for neonatal lung monitoring and nanoparticle tracking. Key research areas include low-power EIT systems, thoracic morphology analysis, and medical imaging algorithm development. His research interests span Medical Imaging, Biomedical Engineering, and Medical Nanoparticle Tracking. Notable contributions include the development of energy-efficient EIT systems for neonatal care and algorithms for breath detection and thoracic shape analysis. Collaborations with institutions like University College London and Dartmouth College highlight his interdisciplinary approach. Publications since 2017 reflect expertise in EIT applications across clinical and biomedical domains. Over 1,400 views and 395 downloads of his works underscore their impact. No scientific awards are listed, though his work contributes significantly to neonatal and pediatric imaging advancements.

Visham RamsurrunView profile

Senior Lecturer

• Cybersecurity
• Internet of Things (IoT)
• Network Security
• +7 more

Visham Ramsurrun is a Senior Lecturer and Programme Coordinator of the MSc Cyber Security programme at Middlesex University Mauritius. He holds a Ph.D. and B.Sc. in Computer Science & Engineering from the University of Mauritius, and a Postgraduate Certificate in Higher Education from Middlesex University (UK). His research focuses on cybersecurity, IoT, network design, machine learning, and smart agriculture. Education: Ph.D. in Computer Science & Engineering (2007-2010), University of Mauritius Postgraduate Certificate in Higher Education (2017-2018), Middlesex University (UK) BSc (Hons) Computer Science & Engineering (2000-2003), University of Mauritius Research interests include: Cybersecurity frameworks for IoT and autonomous systems Blockchain applications in healthcare and data management Network defense mechanisms (e.g., SDN-based moving target defense) Energy-efficient sensor networks and transmission optimization Smart agriculture systems and environmental monitoring Recent publications emphasize cybersecurity in autonomous vehicles, IoT-enabled healthcare systems, and sensor-based home security solutions. He has secured grants from the Mauritius Research and Innovation Council (MRIC) and collaborates with industry partners on innovation projects. His work includes developing augmented reality control platforms and low-cost air quality monitoring systems. As a member of the IPv6 Forum Mauritius, he advocates for network architecture advancements. His research spans academic collaborations across disciplines, with a focus on underserved regions' technological challenges.

Alexandra PsarrouView profile

Associate Professor

• Deep learning methods for Image Quality Performance
• Explainable learning models
• Reinforcement learning
• +3 more

Alexandra Psarrou is a Reader in the School of Computer Science and Engineering at the University of Westminster, serving as Head of School from 2002-2019. She holds a BSc and MSc from Queen Mary University of London and a PhD from Queen Mary, London (1996). Her research focuses on dynamic vision, explainable AI, hyperspectral analysis, and image quality metrics. Education: BSc (1987), MSc (1988), PhD (1996) Roles: Reader, former Head of School, member of multiple University committees Research interests include deep learning, reinforcement learning, and applications in cultural heritage. She founded the Computational Vision Research Group (CVRG) in 1997, advancing techniques like recurrent neural networks and kernel PCA models. Awards include Best Paper Awards at BMVC (1999), IMECS (2008), and IS&T (2020). She led major grants like the NOESIS project (€709k) and diARTgnosis (€518k). Supervised 10+ PhD students in areas like gesture recognition and medical imaging. Labs/Teams: Computational Vision and Imaging Technologies Research Group Grants: NOESIS, diARTgnosis, HISTORIA, HEFCE NFF

Samiya KhanView profile

Lecturer

• Data Science
• Artificial Intelligence
• Edge Computing
• +4 more

Dr. Samiya Khan is a Lecturer in Computer Science at the University of Greenwich, UK, affiliated with the School of Computing and Mathematical Sciences within the Faculty of Engineering and Science. She holds a PhD in Computer Science from Jamia Millia Islamia, India (2020), and is an alumna of the University of Delhi, India. Previously, she served as a postdoctoral research fellow at the University of Wolverhampton, UK. Her expertise spans data science, artificial intelligence, edge computing, IoT, and digital health. Her research contributions include over 25 publications in high-impact journals and conferences, as well as authored/co-edited books such as 'Big Data and Analytics' and 'Internet of Things (IoT): Concepts and Applications' (Springer Nature). She has also contributed as an Associate Editor for Scientific India Magazine and a reviewer for multiple journals and conferences. Samiya’s work emphasizes interdisciplinary applications, including cybersecurity for industrial systems, smart healthcare solutions, and sustainable computing. Her recent research explores XR (Extended Reality) for clinical training, IoT in urban sustainability, and AI-driven telehealth services. Her publications highlight advancements in anomaly detection for critical infrastructure, blockchain-enabled IoT security, and AI frameworks for medical imaging. She has also authored works on big data preprocessing and cloud computing challenges in academia and industry. Key contributions include a book on 'Extended Reality for Healthcare Systems' (Elsevier, 2024) and frameworks for low-cost green computing services. Her research bridges theoretical innovation with practical applications in healthcare, education, and environmental sustainability.