Hey, I’m Nathan. I’m studying for a PhD in particle physics at Lund University (Sweden), specialising in statistics and machine learning. It’s awesome.
Facts about me:
- I’m the self appointed videographer of the INSIGHTS network. I’ll be vlogging our training events to show you how cool it is to be part of a training network, and to showcase my wonderful colleagues ^^
- My hair color is a non-linear function of time.
- I’m British. Love me a nice chips and gravy.
- If I could sum myself up in one GIF, I would use this one:
On an academic level, I’m interested in Bayesian statistical methods, e.g. nested sampling, and applying them to everything physics. By Bayesian, I mean methods that update your prior beliefs about a thing in the light of some data on that thing. This is in contrast with frequentist methods, which try to take a purely ‘data-driven’ approach, telling you about the expected outcome of an experiment in the limit of many identical experiments.
When I ask people in particle physics whether they are Bayesian or frequentist, people often reply along the lines of ‘I use whichever one yields the best result’. I would argue that the two schools of statistics answer fundamentally different questions, so it’s worth sitting down and deciding on a philisophical level the questions you want to ask about your data. More on this to follow in future posts :]
Please enjoy this picture of me dressed as a Christmas tree (left), courtesy of the departmental secret Santa.
When I’m not doing any of this stuff, I make music. I’m releasing one song a week through a project called riverbubble if you want to pass time on a rainy day.
I look forward to making content for you in the future :3