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Dr Stewart Massie

Biography Stewart received his PhD (2006) on Knowledge Management for Case-Based Reasoning Systems from RGU, where he continues to work as a Reader with the Artificial Intelligence (AI) and Reasoning research group. He has more than 15 years research experience in AI developing improved machine learning, information retrieval, recommendation and data mining technologies. His research applies these technologies to the development of applied solutions for text and multi-media applications, as well as more recently for sensor networks. Stewart has published over 90 peer-reviewed papers in leading journals and conferences including Artificial Intelligence, IJCAI and AAAI; and serves as PC member for several international conferences including KSEM, ICCBR, ECIR, EANN and IJCAI.

Scopus - 10242574900

ORCID - 0000-0002-5278-4009
Research Interests Stewart’s research applies AI technologies, including Case-Based Reasoning, Machine Learning and Deep Learning, to the development of intelligent search, recommendation and decision support systems. He plays a leading role in a range of research projects funded in excess of £1 million. Recent funded projects include: tourism applications with Historic Scotland and National Museums Scotland to improve visitor interaction with objects; health applications employing sensors for the self-management of lower back pain, and for predicting increased risk of falls in smart homes; and oil and gas applications to support well construction planning, and to manage the compliance process.
Teaching and Learning Stewart leads postgraduate modules in Information Retrieval Systems and Intranet Systems Development. In addition, Stewart supervises undergraduate, postgraduate and PhD students.
PhD Supervision Availability Yes
PhD Topics Digital tourism and storytelling;
Digital health from smart homes;
Activity recognition from sensor data;
AI support for business process;
Machine and deep learning applied to text or sensors;
NLG in Data-to-Text applications;
Theory or application of Case-Based Reasoning