He is the former Head of the Department of Mathematics at Imperial College London. He served on the Advisory Council for the Office for National Statistics from 1996 to 1998, was Statistical Advisor to the Nuclear Waste Inspectorate from 1991 to 1998 and was advisor on Operational Analysis to the Ministry of Defence from 1982 to 1987. He was Principal of Queen Mary, University of London, from 1998 to 2008
; Director General of Knowledge and Innovation in the Department of Business, Innovation and Skills until 2012; and is now Vice-Chancellor of the University of London. He is a former President of the Royal Statistical Society and is currently a Deputy Chair of the UK Statistics Authority.
He is best known for his work in statistical theory, in particular Bayesian statistics and evidence-based practice. When I interviewed the late Professor Dennis Lindley last year, he called Sir Adrian “the brightest student I’ve ever had.” With Antonio Machi, Smith translated Bruno de Finetti’s Theory of Probability into English. He
wrote an influential paper in 1990 along with Alan E. Gelfand, which drew attention to the significance of the Gibbs sampler technique for Bayesian numerical integration problems. He was knighted in the 2011 New Year Honours.
Statistics Views talks to Sir Adrian about his career in statistics, his memories of the late Professor Lindley, teaching statistics, working on the Smith Report on secondary mathematics education in the UK, the challenges statisticians face, Big Data and how his life may have turned out very differently if he had not chosen statistics.
1) You have an extremely impressive career path including serving on the Advisory Committee to the UK Government Office for National Statistics from 1996 to 1998, working for the UK Government Department of the Environment from 1991 to 1998 as a Statistical Advisor to the Nuclear Waste Inspectorate and for the Ministry of Defence from 1982 to 1987 as adviser on Operational Analysis. What are your memories when you look back on these roles in service to statistics and what do you feel were your main achievements?
In most of those cases, I was invited to join in because I was seen as a statistician and where there were issues on data policy to which I could contribute. I liked that part of my life a great deal. There was a kind of joy to be able to poke one’s fingers into worlds that are not your own, such as burying nuclear waste, which is not something I come across every day. The issues around that – the risks, quantifying the uncertainties – was bread and butter in terms of how I think. What one was doing in each of those areas was utilising the toolkit of thought about uncertainty – communicating and quantifying uncertainty, whether it was burying nuclear waste or considering investments in military equipment – at the heart of all these things were complex systems of uncertainty, so I felt that I was able to contribute.
The challenge really is what is the kind of training and what is the positioning of statistics in its own right as a profession that will continue to be relevant, needed and respected?
I’m still quite lucky now that I have a one-day-a-week role as Deputy Chair of the UK Statistics Authority and within that, I chair the Board that oversees the Office for National Statistics – in particular, the controversies about how you do price indices, RPIs, CPIs, how you measure migration, etc. – I am rather privileged to have a sort of front seat in joining in on those policies and debates and ensuring that the statistical evidence base is as good as it can be.
2) Do you think over the years too much research has focussed on less important areas of statistics? Should the gap between research and applications be reduced? How so and by whom?
What is important research and how you prioritise research is an incredibly interesting area. I spent four years of my life overseeing policies and budgets for how you fund research in the UK. You have a real dilemma and it is neither one thing nor the other – why would governments on behalf of taxpayers spend a lot of money on research if it were not to solve economic, societal or health challenges that contribute to one’s wellbeing? However, the timescale on which solving problems actually leads to positive outcomes in society can be very unpredictable and can only turn out to be very important decades later. So you cannot say “we will do the research and provide you with a solution in three weeks’ time” – it just does not work that way. On the other hand, if all research was not going to yield any outcomes for hundreds of years, the next three or four generations of taxpayers may well think they did not have a very good deal. For me, as an individual researcher and considering the way in which the public approaches topics such as investigative research, there has to be some kind of balance. You have to identify the problems we would really like to solve, such as finding a cure for cancer, for which you can perfectly articulate the case for investment, but it would be completely mad to say that you will find the cure in such and such a period of time.
What you really have to try to ensure is the brightest and the best people having a kind of sense about where the interesting problems are and letting them get on with it. Not everyone can do it – you have to have a substantial portfolio to deliver what you want – and that suited me as an individual. I could be a pure academic in the morning and an applied academic in the afternoon. In retrospect, it will always be the case that huge percentages of research lead nowhere but mainly, you don’t know that until afterwards. It is not easy upfront, and indeed there will also always be grant applications for research that you can put aside straight away with a clear conscience knowing that it would be a waste of time.
3) What do you see as the greatest challenges facing the profession of statisticians in the coming years?
It touches on this issue of Big Data and data science as a more general area. The challenge really is what is the kind of training and what is the positioning of statistics in its own right as a profession that will continue to be relevant, needed and respected? Over the years, in areas such as clinical medicine, statistics has established a key position for itself. The world we are now in where we have huge DNA sequencing and you have millions of pieces of data which if you can mine in the right way, could give you major improvements. Are the key methodological issues of the past still relevant in a world of Big Data? The challenge is for societies such as the RSS to consistently review how the world is changing around them and what we need to do both to be relevant but also, given the expertise and knowledge that statisticians have, how to make sure that is not lost.
4) Are there people or events that have been influential in your career? Also, given that you are one of the most well respected statisticians of your generation and many statisticians look up to you, whose work do you admire (it can be someone working now, or someone whose work you admired greatly earlier on in your career?).
When I started in the world of Bayesian statistics and wanted to become a researcher, I would have to say the big thing that influenced me, not least because it took about a year my life (and completely wiped out my social life!) was translating the work of Bruno de Finetti. He had set himself a huge enterprise really which was an all-encompassing view from a particular standpoint of probability in all its ramifications – mathematical, philosophical, and so on. I admired the scale of that seminal contribution and of course looking back, there wasn’t a subject domain and it had to be forged. People like Jimmie Savage and Dennis Lindley who pioneered the thinking and back before them, de Finetti and Ramsay –those courageous first steps where people do seminal things is something I have always admired.
5) If you had not got involved in the field of statistics, what do you think you would have done? (Is there another field that you could have seen yourself making an impact on?)
When I was at school and deciding where to apply for university, it was at a time post-Cold War when universities were creating for the first time, Departments of Russian. Now when I was applying to Cambridge to study mathematics, I was at a very small country grammar school which was not very well versed on the application procedure, so I did not find out until very late in the day that I had to have studied what is called an inflected language and I had not studied Latin or Greek. So I had to pick something and I picked Russian, which I learnt in two to three months in order to obtain an O-level in order to get into Cambridge. So having done that, I could have gone somewhere to study Russian further. There was an English teacher at my school who’d been trained in Russian immediately after the Second World War and had worked in Germany. He suggested I should study Russian and be amongst the first in a generation to go into Russia.
I was almost certainly a very bad amateur actor and enjoyed acting in plays directed by another of the English teachers at my grammar school who had been a coach at RADA. He said that he thought that he could get me into RADA if I wanted to. So therefore I could have been a foreign office spook or a failed actor but it worked out that I became a statistician in the end.
Copyright: Photograph appears courtesy of Sir Adrian Smith