The title is based on “Elo World, a framework for benchmarking weak chess engines”, and the talk (slides) was timely amid the Carlsen-Niemann saga. The two main ways of modelling performance of human chess players are discussed, with modifications for detection collusion or cheating.

There are two main categories of the cross-section of networks and extremes. The first is extreme values observed at a network on say a 2-d Euclidean space. The second is the extremes of certain characteristics of a network e.g. the degree distribution or other centrality measures. This presentation is on the latter.

Being late to package developments, I wrote the package crandep, not only to capture the dependency network of CRAN packages, but also to analyse it and answer some questions:

In April 2021 Lancaster University held a (virtual) Science Week. I gave a short talk (slide here), thus making my teeny-tiny presence on YouTube.

This presentation is an extension of my review paper on stochastic block models (SBMs). There is this ever-present issue of selecting the number of blocks (or groups) when applying an SBM. This presentation focuses on the approaches that use a criterion to select this number. Various criteria that existed then are:

A taster lecture was given to high school students who want to do STEM degrees in university. It was done virtually (of course) at the height of the pandemic and first lockdown, and I thought, what better topic to talk about than epidemics? I didn’t know how the students took it though, but there was at least a differential equation to work on.