Ellis Solaiman
Reader in Computer Science · School of Computing
Newcastle University · United Kingdom简介
Ellis Solaiman is a Reader in Trust and Resilience in Computer Science at the School of Computing, Newcastle University. He received his PhD in Computing Science from Newcastle University, where he subsequently held positions as Research Associate, Teaching Fellow, Lecturer, Senior Lecturer, and Reader. He is Co-Lead of the EPSRC-funded National Edge AI Hub for Real Data and serves as National Research Theme Lead for Data-Sensitive Edge AI Validation. His research focuses on trusted and resilient intelligent distributed systems, integrating Machine Learning, Explainable AI, Blockchain, and Smart Contracts. He investigates transparency, accountability, provenance, and verification in distributed socio-technical systems. He also works on sustainability and circular supply chains in the built
教育经历
- PhD Computing Science, Newcastle University
代表成果
- Booth A, James P, Solaiman E. Towards Democratising Urban Sustainability Data: An LLM-Enabled Natural Language Interface for Smart-City Air-Quality Decision Support. Sustainability 2026
- Adu-Duodu K, Wilson S, Li Y, Rana O, Wang Y, Ranjan R, Shah T, Solaiman E. Human-in-the-loop semantic middleware for construction compliance checking and safe product reuse. Advanced Engineering Informatics 2026
- Wilson S, Adu-Duodu K, Li Y, Solaiman E, Rana O, Ranjan R. Exploring Blockchain Interoperability: Frameworks, Use Cases, and Future Challenges. Systems 2026
- Alzubaidi Ali, Albshri Adel, Mitra Karan, Ranjan Rajiv, Solaiman Ellis. SimBlockLink: A Middleware for Linking IoT Simulations with Real Blockchain Platforms for Enhanced Performance Evaluation. Blockchain: Research and Applications 2025
- Alsharidah AA, Jha DN, Solaiman E, Wei B, Aujla GS, Ranjan R. REWARDCHAIN: A Blockchain-Based Incentive Mechanism for Federated Learning in Consumer-centric Internet of Medical Things. IEEE Transactions on Consumer Electronics 2025
- Booth A, James P, McGough S, Solaiman E. Cross-Regional Deep Learning for Air Quality Forecasting: A Comparative Study of CO, NO2, O3, PM2.5, and PM10. Forecasting 2025
- Solaiman E, Awad C. Trust and Dependability in Blockch-AI-n Based MedIoT Applications: Research Challenges and Future Directions. IT Professional 2024
数据校验于 9/6/2026数据来源