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<h2 itemprop="name">Samuel Davenport</h2>
<h3 itemprop="jobTitle">PhD Student on the Oxford-Warwick Statistics Program</h3>
<h3 itemprop="jobTitle">Affiliated with the Big Data Institute</h3>
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<h3>Interests</h3>
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<li>Random Field Theory</li>
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<li>Multiple Testing</li>
<li>Selective Inference</li>
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<p class="course">PhD in Statistics, Oxford-Warwick Statistics Programme, 2016-present</p>
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<p class="course">Masters in Mathematics (Part III) 2015-2016</p>
<p class="institution">University of Cambridge, UK</p>
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<p class="course">Mathematics Tripos 2012-2015</p>
<p class="institution">University of Cambridge, UK</p>
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<p>I am a 3rd year PhD student at the Department of Statistics at the University of Oxford on the Oxford-Warwick Statistics Programme (OxWaSP) supervised by <a href="https://www.nisox.org" title="Thomas Nichols' Homepage" target="_blank">Thomas E. Nichols</a> and
<a href="https://scholar.google.co.uk/citations?user=zBx_e0EAAAAJ&hl=en" title="Armin Schwartzman's Google Scholar Page" target="_blank">Armin Schwartzman</a> and affliated with the
Big Data Institute. I work on developing statistical methods with applications in Neuroimaging, Astrophysics and beyond. More information can be found in my <a href="/docs/other/cv.pdf">CV</a>.</p>
<h2 id="research-interests">Research Interests</h2>
<p>My interest in Random Field Theory (RFT) stems from the huge potential for application across a variety of fields and the beauty of the underlying mathematics.
Recently this has been a matter of controversy in the neuroimaging community, primarily because RFT methods have been applied widely without proper regard for the underlying assumptions.
However it turns out that a number of these assumptions are unnecessary and can be dropped (by modifying the theory). There is much recent (and ongoing) work on RFT and alternative methods that
provide correct control of the false positive rates and allow for better, more powerful inference.
</p>
<h2 id="research-interests">Big Data Validation</h2>
<p>
In general I am keen to encourage rigorous testing of statistical methods in brain imaging using large datasets. My work on selective inference has looked at providing selection corrected estimates for the effect size in fMRI and VBM data. This has been tested using a validation framework that uses data from the UK biobank and involves setting aside a large number of subjects to calculate a ground truth and dividing the remaining subjects into small groups (of sizes representative of those used in typical brain imaging studies) in order to compare methods. It is vital that all existing and emerging statistical methods be validated in this manner so that we can ensure that our methods work as well as is claimed. For more details see our preprint.
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