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Areas of Consultancy: Bayesian methodsDesign and analysis of experimentsExploratory data analysisGLMs and other non-linear modelsMultivariate analysisNumerical analysis and optimisationPattern recognition and image analysisStatistical inferenceTime series
Region of consultancy: UK
Daniel is a Professor at the University of York, and a Chartered Statistician. He has taught research methods for many years to University students, in addition to being an active researcher with over 80 publications. In 2022 he published a textbook, Research Methods Using R, with Oxford University Press.
Daniel graduated in 2003 with a BSc in Psychology from the University of Nottingham. After a brief period in industry working at an IT consultancy firm, he then completed a PhD in human visual perception at Aston University, Birmingham. He next worked for five years as a postdoctoral researcher, before joining the University of York in 2013. Daniel's research interests are primarily in low level human vision, using a range of techniques including EEG, MRI, psychophysics and computational modelling. He has also published statistical work on power analysis and multivariate techniques.
Analysis of empirical data from studies using EEG, MRI, MEG, eye tracking, psychophysiology, behavioural methods, psychophysics. Computationally reproducible data analysis pipelines, including integration with online repositories. Bespoke computational modelling including with Bayesian hierarchical models. Machine learning and pattern classification. Frequency-based (Fourier) analysis, and multivariate analysis of spectral data. Data visualisation, including procedurally generated publication quality figures. Advice and troubleshooting for experimental design and data analysis. Workshops on statistical analysis using the above techniques, tailored to specific requirements.