Mark is a psychologist and statistician whose work lies at the intersection of cognitive science, Bayesian data analysis, and applied statistics. His research focuses on developing and testing Bayesian models of human cognition, with a particular emphasis on language processing and memory. He also works extensively on the theory and application of Bayesian statistical methods in the social and behavioural sciences, bridging methodological advances with real-world research challenges.
Since 2015, Mark has co-led a programme of intensive workshops on Bayesian data analysis for social scientists, funded by the UK’s Economic and Social Research Council (ESRC). These workshops have trained hundreds of researchers in the practical application of Bayesian methods, particularly through R and modern statistical packages.
Education & Career
• PhD in Psychology, Cornell University, New York (Cognitive Science, Bayesian Models of Cognition)
• MA in Psychology, Cornell University, New York
• BA (Hons) in Psychology, National University of Ireland
• Senior Lecturer in Psychology, Nottingham Trent University, England
Research Focus
Mark’s work centres on:
• Bayesian models of human cognition, especially in language processing and memory
• General Bayesian data analysis methods for the social and behavioural sciences
• Comparative studies of Bayesian vs. classical approaches to inference and model comparison
• Promoting reproducibility and transparent statistical practice in psychological research
Current Projects
• Developing Bayesian cognitive models of memory and linguistic comprehension
• Exploring Bayesian approaches to regression, multilevel, and mixed-effects models in psychology and social science research
• Co-leading ESRC-funded workshops on Bayesian data analysis for applied researchers
Professional Consultancy & Teaching
Mark provides expert training and advice in Bayesian data analysis for academic and applied research projects. His teaching portfolio includes courses and workshops on:
• Bayesian linear and generalized linear models
• Multilevel and mixed-effects models
• Cognitive modelling with Bayesian methods
• Applied statistics in R for psychologists and social scientists
He is also an advocate of open science and is experienced in communicating complex statistical methods to diverse audiences.
Teaching & Skills
• Instructor in Bayesian statistics, time series modelling, and machine learning
• Strong advocate for reproducibility, open-source tools, and accessible education
• Skilled in R, Stan, JAGS, and statistical computing for large datasets
• Experienced mentor and workshop leader at all academic levels
Links
• University Profile
• Personal Page
• ResearchGate
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