Rate My Professor Mike Chanter

Mike Chantler is Professor of Computer Science in the School of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh. He earned a first-class honours degree in Electrical and Electronic Engineering from the University of Glasgow in 1979 and a PhD in image processing from Heriot-Watt University in 1994. After working as a software engineer in the computer industry, he joined Heriot-Watt’s Electrical Engineering Department and advanced to his current professorial position. With over 35 years of experience in AI, robotics, and software engineering, Chantler leads the Strategic Futures Laboratory, which develops and deploys AI tools for strategic insight, evidence-based, and auditable decision-making. He has supervised more than 25 PhD students, published over 150 peer-reviewed papers, chaired leading international conferences and workshops, secured multi-million pounds in EU and national funding, and spun out two companies.

Chantler’s research focuses on computer vision, human perception and interaction, machine learning to extract meaning from data while promoting user trust via transparent data provenance, psychophysics, data visualisation, and ethical crowd-sourcing. Current projects include strategic thematic summaries of large project portfolios and smart support for research landscaping and strategy development meetings involving up to 100 stakeholders. He developed the Well Sorted tool for creating and structuring research landscapes and brainstorming meetings. Chantler has shaped UK research strategy through service on numerous EPSRC, ESRC, Data Lab, and InnovateUK prioritisation panels, including the EPSRC ICT Strategic Advisory Team and Digital Economy’s Portfolio Advisory Board. Awards include Honourable Mentions at ACM CHI 2017 and ACM DIS 2017, Research Team of the Year 2022, and Visualising Covid-19 Research improved by Google Cloud 2020. Key publications are “A Review of LLM-Assisted Ideation” (2025), “The Importance of Phase to Texture Discrimination and Similarity” (2020), “Visualising COVID-19 Research” (2020), “Survey of Procedural Methods for Two-Dimensional Texture Generation” (2020), and “A Two-Stage Learning Framework with a Beam Image Dataset for Automatic Laser Resonator Alignment” (2026).