Jim Smith

Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL.

• University: University of the West of England
• School: School of Computing and Creative Technologies
• Department: Department of Computer Science and Creative Technologies
• Role: Professor in Interactive Artificial Intelligence
• Leadership: Director, Computer Science Research Centre

Research Interests: Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models.

Recent Publications: His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing.

• Machine Learning & Privacy
• Evolutionary Computation
• Interactive AI Systems
• Human-Computer Interaction
• Statistical Disclosure Control
• Federated Learning Security

Scientific Awards: No specific awards are mentioned in the provided texts.

Advising and Grants: He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research.

• Interactive Machine Learning for Claim Settlement (Innovate UK)
• SDC-Reboot (DARE UK/Health Data Research UK)
• Threat Identification in Mobile Networks (Ribbon Communications)

Labs and Teams: He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.