Marta Blangiardo: Professor of Biostatistics, Imperial College London
While studying for her doctorate During my PhD Marta became 'completely mesmerised by Bayesian statistics'. Since then she has developed a high-flying career. We asked her to tell us more about her achievements and her views on how other women can be supported to flourish in health data science.
Tell us a little about yourself and your background
I grew up in Italy, where I completed my studies. I have a degree in Statistics, Demography and Social Sciences from the University of Milan-Bicocca and a PhD in Statistics from the University of Florence, where I worked on statistical modelling for gene expression data. During my PhD I first came into contact and become completely mesmerised by Bayesian statistics, which I thought was such a natural way of thinking about probability.
During a conference I met “my hero” (who happened to be a woman) and we got to talk about my research. I loved that she was so approachable while being such a huge name in my research field. A few months later she contacted me saying that she had a research associate position to work with her and I thought “this must be a dream”. I applied and got the job and so my career at Imperial College began. I have been there for 17 years, moving on as lecturer, senior lecturer and reaching my current role as professor in the summer of 2018.
Tell us about your current role
I am part of the Department of Epidemiology and Biostatistics within the School of Public Health. I lead the environment and health statistics group and the Biostatistics and Data Science theme of the MRC Centre for Environment and Health. My main interest is in Bayesian modelling of spatial and spatio-temporal data, applied mainly to on environmental exposures (e.g. air pollution, noise) and on assessing their health effects.
What led you to enter health data science?
I have always loved mathematics and have been fascinated by the concept of uncertainty and probability since middle school. So when I had to choose my university degree, statistics was the obvious choice. During my degree I was drawn to epidemiology and to the idea of working to help improve population health. So health data science combines these two long standing interests perfectly.
What have been some of your proudest achievements?
Back in 2011, I decided with a colleague and good friend of mine to embark on writing a book on the Integrated Nested Approximation Methods for spatial and spatio-temporal applications as there was not much documentation on the topic back then. Holding a copy of the book four years later was one of my proudest moments. Also, the feeling of accomplishment when I got my first MRC grant was hard to beat. I am also extremely proud of the work I have been doing over the past 2 years as part of the COVID Turing-RSS Health Data Lab, as it has been an amazing opportunity to directly engage with UKHSA and to see how academic work can directly and quickly influence policies.
Have you encountered – or do you anticipate – particular challenges from being a woman in your field?
While it is great to see so many women working in statistics and epidemiology, I feel that unconscious bias is something we still have to battle against and generally we still have to shout louder to be heard.
Have there been any positive examples of being encouraged or made welcome?
I have been fortunate to work with amazing people over the years, most of whom have been women who have provided great examples and have always been very supportive. For a woman it might be especially hard at the beginning of the academic career, to balance work and personal life and it can really make a difference to perceive that senior colleagues understand the situation, and maybe had even to overcome the same challenges.
What’s the best piece of advice you have received and/or the one piece of advice would you give to other women entering your field?
Be bold and do not be afraid of making mistakes.
What one thing would you like to see happen to ensure that women can have flourishing careers in health data science?
Leadership training directed to women and mentorship/peer-to-peer support groups are extremely helpful for women’s career. At the same time, I do not think we train men specifically, for instance against unconscious biases and I feel this would provide a step forward to make sure that women have equal opportunities to embrace the career they deserve.