I’m currently a third year PhD Student in the lab of Wolfram Schultz at the University of Cambridge. I study decision making in simple economic tasks in non-human primates, and in my spare time enjoy practising my coding- mostly in R. This website serves as a hub for small pieces of work relating to this.
Currently looking for jobs related to analytics/ data science, starting within the next ~3 months. My CV is listed at the top.
#for working with polygons library(sf) library(sfheaders) library(tidyverse) library(gtools) set.seed(22081992) Riddler Express This weeks express deals with an erratic driver:
In Riddler City, the city streets follow a grid layout, running north-south and east-west. You’re driving north when you decide to play a little game. Every time you reach an intersection, you randomly turn left or right, each with a 50 percent chance.
After driving through 10 intersections, what is the probability that you are still driving north?
Every Tuesday, the R4DataScience community posts a dataset online as part of #TidyTuesday as practice wrangling and modelling data. For the week of 5th May 2020, the dataset concerned the video game Animal Crossing.
Intro Radiohead - How I Made My Millions
I don’t play Animal Crossing (unfortunately Nintendo Switches sold out as the UK went into lockdown), but it seems that everyone around me does so I’ve become fascinated by how it has created almost a surrogate life for people, performing manual tasks to pay off loans to Tom Nook, the nefarious bankster of the player’s island.
I wrote this one pretty quickly compared to part 1 (which goes much deeper into mathematical concepts), and only realized after how much of a similarity it has to many of Ben Torvaney’s posts on the subject. This probably isn’t a coincidence given how much I’ve used his work previously in posts on this blog. Any imitation here is meant as flattery. The purpose of this post is really as a bridge between what I really want to write about- the maths behind the models in part 1, and extensions of these models into other distribution in parts 3-n so it might be a little derivative of stuff written elsewhere.
written during lockdown so while I think it adds some value (and is useful to organise my thoughts on the paper for my own work on football) there are probably mistakes. E.g. the C++ code is still pretty inefficient and could well be improved and I’ve surely confused some maths concepts. To be honest, the post is just an excuse to practice writing LaTeX maths and some C++. Let me know my errors and I’ll correct
generally as I have less and less time to waste on meaningless football stats I get halfway through a post and abandon it. To remedy this, I want to start pushing out posts that give a reasonable half-guess at an answer within an hour or so without needing to really check my working or write good prose. This is the third of these
For this weeks question, I’m stealing straight from the source of most of my posts, The Knowledge column at The Guardian: What is the shortest total distance a club has had to travel in a Champions League winning campaign?
ggparliamentis useful research tool for a variety of social science disciplines, includ-ing quantitative political science. It is particularly beneficial for political scientists whoresearch political institutions, such as electoral systems, party politics, or legislative pol-itics.ggparliamentprovides several layouts, representing different legislative chamberse.g. the United Kingdom’s House of Commons, Australia’s horseshoe-shaped parliament,or the widely-used semicircle legislative chamber.
The cellular basis of the magnetic sense remains an unsolved scientific mystery. One theory that aims to explain how animals detect the magnetic field is the magnetite hypothesis. It argues that intracellular crystals of the iron oxide magnetite (Fe3O4) are coupled to mechanosensitive channels that elicit neuronal activity in specialized sensory cells. Attempts to find these primary sensors have largely relied on the Prussian Blue stain that labels cells rich in ferric iron…
NB Edelman, T Fritz, S Nimpf, P Pichler, M Lauwers, RW Hickman, A Papadaki-Anastasopoulou, L Ushakova, T Heuser, GP Resch, M Saunders, JA Shaw, DA Keays
In 2018, after watching the CONIFA World Cup final live, I wondered if an Independent Yorkshire could win the FIFA World Cup. This resulted in a few blogposts that were turned into an article in Citymetric magazine
In Summer 2019, I won the chance to explore a hypothesis in football analytics using data from Statsbomb. My final project looked at Markov chain models of possession value in football, and considering how to incorporate defensive risk into such models.
