Alison Gray

Dr. Alison Gray is a Lecturer in the Department of Mathematics and Statistics within the Faculty of Science at the University of Strathclyde. She is a member of the Population Modelling and Epidemiology (PME) research group and an associate member of the Stochastic Analysis group, contributing to both teaching and interdisciplinary research.

Her research interests are centered on statistical applications in real-world contexts, particularly in pattern recognition, machine learning, image analysis, applied epidemiology, stochastic differential equation (SDE) models for epidemics, and statistics in honey bee research. She has led long-term studies on honey bee colony losses in Scotland since 2006 and is deeply involved in the international COLOSS network, serving as co-chair of the colony loss monitoring group since 2014 and data coordinator since 2016, coordinating surveys across over 30 countries. She also led the COLOSS CSI Pollen citizen science project in the UK and internationally.

Her recent publications (2024–2025) reflect a strong focus on global honey bee health, including colony dynamics in Ethiopia, multi-country loss surveys in Africa, and the evaluation of AI-generated beekeeping advice via ChatGPT. These works demonstrate a consistent application of statistical methods to ecological and agricultural challenges, particularly in apiculture and environmental monitoring.

Scientific Awards:

• Fellow of the Royal Statistical Society

She has been principal or co-investigator on multiple research projects, including those funded by the European Commission (Horizon Europe) and the British Beekeepers' Research Trust, focusing on pesticide monitoring, forage diversity, and aquaculture. While specific student names are not listed, she has supervised academic work as noted in her research profile. She is actively involved in professional activities, including invited talks and workshops related to bee colony monitoring.

Dr. Gray leads a current project mapping bee races across Scotland using wing morphometry, multivariate statistics, and DNA analysis, continuing her integration of advanced statistical techniques with ecological fieldwork.