Rate My Professor James Smith

Professor James Smith is Professor in Interactive Artificial Intelligence in the School of Computing and Creative Technologies, Faculty of the Environment and Technology, at the University of the West of England, Bristol. He is Director of the Computer Science Research Centre and leads the AI@UWE research theme. Smith first came to UWE Bristol in 1993 as a postgraduate student, where he completed an MSc followed by a PhD in Artificial Intelligence. In 1996, he joined the staff as a Research Fellow, advancing through Senior Lecturer to his current professorial role. He holds an MA from the University of Cambridge, an MSc, and a PhD. As a Senior Fellow of the Higher Education Academy, he has extensive experience in teaching artificial intelligence to undergraduate and postgraduate students, leading modules such as Artificial Intelligence 1 and Advanced Artificial Intelligence, and contributing to programme development.

Smith's academic interests encompass interactive artificial intelligence, machine learning, evolutionary computation, machine vision, and statistical disclosure control, particularly privacy leakage from AI models and preservation of privacy when using sensitive data like health records for public good research. He directs several funded initiatives, including UKRI-supported GRAIMATTER, SACRO, SDC-Reboot, and TREvolution projects within the DARE UK programme; an Innovate UK collaboration with Lyons Davidson Ltd. on interactive machine learning for predicting insurance claim settlement values; and a project with Ribbon Communications Ltd. on detecting security threats in large-scale mobile networks. Key publications include the second edition of the textbook Evolutionary Computation, co-authored with Gusz Eiben in 2015, and the article 'From evolutionary computation to the evolution of things' in Nature (2015). He currently supervises PhD students on convolutional neural architectures for air quality modelling, interactive machine learning for network security, and data privacy in federated learning. His work bridges theoretical AI advancements with practical applications in industry and government.