CORONAVIRUS NOTE: I am currently working on coronavirus. Read more about our group's work here. I am unavailable for interview. For media enquiries relating to the NHS COVID-19 app, consider reaching out to the app's communication team, or to Christophe Fraser, Luca Ferretti, Lucie Abeler-Dorner, David Bonsall, or Michelle Kendall.

After a PhD and some postdoctoral research in theoretical particle physics, I decided to join Christophe Fraser's group, at Imperial College London 2014-2016 and subsequently here in the Big Data Institute. I work on HIV genomics and epidemiology as part of the BEEHIVE project: analysing viral genetic data, in conjunction with patient clinical data, to gain insight into the viral-molecular basis of infection severity.

I wrote the tools shiver for reconstructing HIV (and other viral) genomes from short read data, and phyloscanner (with Matthew Hall and Christophe) for analysing within- and between-host pathogen genetic diversity. Here are some slides about shiver and phyloscanner and related ideas; here's a webinar in which I go through some of those slides. A few other bits of my code for working with sequence data are here.

My publications are here. (Here is an open-access link to the only one of those that's paywalled, on academic institutional policy on flying: Jean and Wymant, Science 2019.)

While at Imperial I taught Core Mathematics for masters courses in public health and epidemiology. Here is some material I wrote for this: the quiz testing all of the material covered, and the answers; basic manipulation of numbers - the slides for 2015 and prose-style notes from 2014; inequalities, functions and units - slides for 2015, solutions to the problems, and prose-style notes from 2014; probability parts one and two; calculus in more detail; a bit of supplementary material about trigonometric, exponential and logarithmic functions, and matrices.

Assorted useful links:

• Read this if you write in English. Read this if you write in order to make a point.
• Read this if you give talks.
• Read this if you're a scientist using a computer.
• These resources were recommended for learning to use the command line (a.k.a. the terminal a.k.a. the shell), which is an important step in being able to use other people's computational scientific methods: http://rik.smith-unna.com/command_line_bootcamp
  http://www.ee.surrey.ac.uk/Teaching/Unix
  http://swcarpentry.github.io/shell-novice/.
• This was highly recommended for learning version control with Git (aimed at users of R but with more general applicability).
• This helps one remember obscure latex symbols.

• Here are some bash commands (i.e. working with the terminal / command line) that I find helpful.