Why did you decide to join the Research Engineering Group?
Stumbling across the job ad was a piece of good luck – I’d been looking for seminars to attend in London while writing up my thesis and noticed that the Turing was hiring. I’ve always been a field-switcher (first a physicist, then a computational biologist) because I love the fresh feeling of picking up something new.
The flexibility of the Research Data Scientist role really appealed to me. When starting a new project, the group actively encourages people to have either the programming language, domain area or technical approach be new to them. Also, we have fantastic teammates who are always there to give advice when needed.
What are you working on at the moment?
I’m actually in-between projects at the moment, so it’s either a very short answer or a longer one – I’ll go with the latter!
I’ve spent the last couple of months supporting the DECOVID project. DECOVID brings together several different teams of analysts to answer research questions using clinical data about patient care during the COVID-19 pandemic. With a lot of data, regularly updated code and several teams working generate insights, it was important we could check that each team could consistently produce the same results each time they ran their code. To prepare for supporting the analysts working on the data, we developed a tool, repro-catalogue, that records the state of the input data, code, and results each time the analysis pipeline is run. The output of the tool can be used to quickly identify whether the pipeline is reproducible or not.
I’ll be working on new projects soon. Firstly, the Vehicle Grid Integration project, where our first task will be to help the research team access data about electric vehicle charging and the electricity grid. The data needs to be held securely, but the user experience is still important. I’ll work with my colleague Anna to develop a cloud-based solution so that the researchers can easily pull in the data they require. My other project will explore and quantify the uncertainty in simulations of smart cities.
What’s the most useful tool or technique that you’ve picked up since joining the Turing?
I’ll go with Docker for this one – the first time I heard about it was during my Turing interview. I really liked the sound of it once my interviewer (my colleague May) explained more. I’ve since used it in several projects, and it’s incredibly useful for specifying and sharing all the dependencies that you need when working on larger software projects.
I should also mention the Jupyter Book project. We use the format in the Turing Way (a community-built handbook about best practices in data science), and it’s fantastic how you can so easily mix text and interactive content in a clean format.
How would you describe your work in three words?
Exploratory – Collaborative – Sustainable
What do you enjoy most about working in London?
I find the bustle of the city really energising; there’s always something new to see and do here. The Turing is so near the city centre that it’s easy to go out after work. The British Library itself is also an amazing space and the buzz carries through into the Turing itself too. There are always (well, in pre-COVID days!) lots of visitors around, and it’s impossible to enter the kitchen area without hearing an interesting discussion.