Tom Maullin
Postdoctoral Researcher
in fMRI Statistics
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About me
I am currently a postdoctoral researcher at the Big Data Institute in the University of Oxford. My areas of research interest include distributed machine learning, spatial statistics, linear mixed models, and random field theory.
My recent research has been centered on establishing probabilistic bounds for random excursion sets and introducing novel derivations for mixed model parameter estimation in my publications [1], [2], and [3]. Coding projects I have worked on include developing software tools for conducting large-sample fMRI linear mixed model and linear model analyses, as well as creating spatial confidence regions for fMRI conjunction inference. I have also made contributions to several established fMRI software packages, such as the SwE and IBMA toolboxes. For more information, please see this page.
- Linear Mixed Models
- Random Field Theory
- FMRI Inference
- Spatial Statistics
- Maullin-Sapey, T., & Nichols, T. E. (2021). Fisher scoring for crossed factor linear mixed models. Statistics and Computing, 31(5), 53. https://doi.org/10.1007/s11222-021-10026-6
- Maullin-Sapey, T., & Nichols, T. E. (2022). BLMM: Parallelised computing for big linear mixed models. NeuroImage, 264, 119729. https://doi.org/10.1016/j.neuroimage.2022.119729
- Maullin-Sapey, T., Schwartzman, A., & Nichols, T. E. (2022). Spatial Confidence Regions for Combinations of Excursion Sets in Image Analysis. arXiv. https://doi.org/10.48550/arxiv.2201.02743