Brieuc Lehmann (he/him)
Lecturer (Assistant Professor) University College London
Contact: Email: b.lehmann@ucl.ac.uk
Twitter: @brieuclehmann
Website: brieuclehmann.github.io
What does your job actually entail? I’m a lecturer / assistant professor in Statistical Science at UCL, which means I split my time between teaching and research. I’m also (partly) responsible for the departmental twitter account @UCL_stats! Teaching covers a number of things: designing course material, giving lectures, marking assessments, as well as supervising awesome Masters and PhD students for their own research projects. I really enjoy teaching and find it hugely rewarding (OK, maybe not so much the marking). The main focus of my research is on using statistics and machine learning to better understand health inequalities and to advance health equity. More on that below, but in terms of what this actually means day-to-day - it really varies, and that’s one of the reasons I love doing research! One moment I might be reading the latest paper on a clever machine learning algorithm, next I might be writing R code to analyse some data from UK Biobank, then I might have a meeting with my wonderful collaborators to bounce around some ideas, and I might finish all that off by prepping some pretty figures and slides to present my work at the next conference I’m attending.
When not at work you can be found...
Running! I try to get out every day, rain or shine. And I’m obsessed with parkrun. Parkruns are free, community-led 5km runs held every Saturday at 9am in parks ACROSS THE WORLD. They’re super friendly and welcoming - yes you do get some super-speedy runners but you’ve also got the brisk walkers, buggy-pushers and the poor souls being dragged around by over-excited dogs. Plus the amazing volunteers providing encouragement all the way round the course. It’s my favourite way to start the weekend!
Why DSxHE? The idea for DSxHE came a few months into the pandemic. It quickly became clear that COVID-19 was (and still is) having a highly unequal impact both within societies and across borders. The reasons for this are incredibly complex, involving a web of social, cultural, economic, environmental and genetic factors. The pandemic has also been a microcosm of the intricate role that data science plays when it comes to health and healthcare. On one hand, unprecedented access to data (e.g. the UK Coronavirus Dashboard) has enabled local health officials to deploy better targeted interventions and allowed everyone to track the progress of coronavirus. On the other hand, there have been examples of digital tools actually making health inequalities worse, such as the case of racial bias in pulse oximeters. Of course, health inequalities are nothing new: the pandemic has simply brought long-standing disparities into even sharper focus. We now live in a world, however, where data-driven technologies are being used more and more widely in our day-to-day lives, not least within health systems. To make sure that this digitisation works to reduce disparities and that existing inequalities are not worsened, we need to actively take into account issues of bias, transparency, and fairness. And the breadth and the complexity of the challenges faced demands a cross-sector and inter-disciplinary approach, with engagement and collaboration between those at all stages of the data pipeline. Which is where DSxHE comes in! To act as friendly, inclusive, and impactful space bringing together experts, enthusiasts and hobbyists working at the intersection of data science and health inequalities to ensure that the latest research and innovations improve health equity.
What’s a topic in data science/health equity that you know/care a lot about - why is it important/interesting, tell us about it! Statistics! I’m a statistician by training and I spend a good chunk of my time thinking about how we can use data to promote health equity. My research focus is mostly on methodology - that is, developing new statistical methods that are able to handle the realities and intricacies of the data we have at hand, enabling us to answer questions that we weren’t able to before. For example, health research data has a strong bias towards WEIRD (Western, educated, industrialized, rich and democratic) societies. This lack of diversity in health data is means that our sample is not representative of the population of interest, and we need new statistical methods that reflect this to make sure we don’t draw false conclusions from the data. Also, I love discovering new datasets, so if you have some data that you’re not sure what to do with - please get in touch!
What’s a recent article/book/video/blog/event you’ve come across on data science and/or health equity that you found interesting and why? I recently attended a webinar based on health equity guru Sir Michael Marmot’s excellent book: The Health Gap: The Challenge of an Unequal World. It was a fascinating, wide-ranging discussion that covered a lot of ground (you can watch the recording here). One thing that particularly struck me was Sir Michael’s rhetorical question on the role of rebels and activists: ‘We've just had a COP 26 for climate change - when are we going to have COP 1 for health inequalities?’
What is not a big deal to most people but is torture to you?
I can’t eat raw apples - the texture sends shivers down my spine... Even someone chomping on an apple too close to me gives me the heebie-jeebies.
What is one of your nicknames?
Dave.
What never fails to make you laugh?
This video of a famous snooker player failing at virtual reality snooker - IT GETS ME EVERY TIME.