I left CASA in UCL in July 2016 and moved to the new Alan Turing Institute. I’d planned the move to give me a new research environment – as a Fellow with some responsibility for developing an ‘urban’ programme. There were few employees – most of the researchers – part-time academics as Fellows, some Research Fellows and PhD students – were due in October. I ran a workshop on ‘urban priorities’ and wondered what to do myself with no supporting resources. I was aware that my own research was on the fringes of Turing priorities – ‘data science’. I could claim to be a data scientist – and indeed Anthony Finkelstein, then a Trustee and a UCL colleague – in encouraging me to move to Turing said: “You can’t have ‘big data’ without big models”. However, in Turing, data science meant machine learning and AI rather than modelling as I practised it. So, I started to think about a new niche: Darwin in his later years decided to work on ‘smaller problems’, perhaps more manageable. I’m not comparing myself to Darwin, but there may be good advice there! And as for machine learning, though I put myself on a steep learning curve to learn something new and to fit in, though I couldn’t see how I could manage the ‘10,000 hours’ challenge that would turn me into a credible researcher in that field.
At the end of September, everything changed. In odd circumstances – to be described elsewhere – I found myself as the Institute CEO. There was suddenly a huge workload. I reported to a Board of Trustees, there were committees to work with, there were five partner universities to be visited. Above all, a new strategy had to be put in place – hewed out of a mass of ideas and forcefully-stated disagreements. Unsurprisingly, this blog was put on hold. I can now begin to record what I learned about a new field of research (for me) and the challenges of setting up a new Institute. I had to learn enough about data science and AI to be able to give presentations about the Institute and its priorities to a wide variety of audiences. I was able to attend seminars and workshops and talk to a great variety of people and by a process of osmosis, I began to make progress. I will start by recording some of my own experiences of collaboration in the Institute.
The ideal of collaboration is crucial for a national institute. Researchers from different universities, from industry, from the public sector, meet in workshops and seminars, and perhaps above all over coffee and lunch in our kitchen area, and new projects, new collaborations emerge. I can offer three examples from early days from my own experience which have enabled me to keep my own research alive in unexpected ways. (There are later ones which I will return to in due course.)
I met Weisi Guo at the August 2016 ‘urban priorities’ workshop. He presented his work on the analysis of global networks connecting cities which demonstrated the probabilities of conflicts. Needless to say, this turned out to be of interest to the Ministry of Defence and through the Institute’s partnership in this area, a project to develop this work was funded by DSTL. It seemed to me that Weisi’s work could be enhanced by adding flows (spatial interaction) and structural dynamics and we have worked together on this since our first meeting. New collaborators have been brought in and we have published a number of papers. From each of our viewpoints, adding research from a different previously unknown field, has proved highly fruitful.
The second example took me into the field oh health. Mihaela van der Schaar arrived at Turing in October from UCLA, to a Chair in Oxford and as a Turing Fellow. One of her fields of research is the application of machine learning to rapid and precise medical diagnosis. This is complex territory involving the accounting of co-morbidities as contributing to the diagnosis and prognosis of any particular disease, and having an impact on treatment plans. I recognised this as important for the Institute and was happy to support it. We had a lucky break early on. I was giving a breakfast briefing to a group of Chairs and CEOs of major companies and at the end of the meeting, I was approached by Caroline Cartellieri who thanked me for the presentation but said she wanted to talk to me about something else: she was a Trustee of the Cystic Fibrosis Trust. This led to Mihaela and her teams – mainly of PhD students – carrying out a project for the Trust which became an important demonstration of what could be achieved more widely – as well as being valuable for the Trust’s own clinicians. For me, it opened up the idea of incorporating the diagnosis and prognosis methods into a ‘learning machine’ which could ultimately be the basis of personalised medicine. And then a further thought: the health learning machine is generic: it can be applied to any flow of people for which there is a possible intervention to achieve an objective. For example, it can be applied to the flow of offenders into and out of prisons and this idea is now being developed in a project with the Ministry of Justice.
Mihaela’s methods have also sown the seed of a new approach to urban modelling. The data for the co-morbidities’ analysis is the record over time of the occurrence of earlier diseases. If these events are re-interpreted in the urban modelling context as ‘life events’ -from demographics – birth migration and death – but to include entry to education, new job, new house and so on, then a new set of tools can be brought to bear.
The third example, still from very early on, probably Autumn 2016, came from me attending for my own education a seminar by Mark Girolami on (I think) the propagation of uncertainty – something I have never been any good at building into urban models. However, I recognised intuitively that his methods seemed to include a piece of mathematics that would possibly solve a problem that has always defeated me: how to predict the distribution of (say) retail centre sizes in a dynamic model. I discussed this with Mark who enthusiastically agreed to offer the problem to a (then) new research student, Louis Ellam. He also brought in an Imperial College colleague, Greg Pavliotis, an expert in statistical mechanics and therefore connected to my style of modelling. Over the next couple of years, the problem was solved and led to a four-author paper in Proceedings A of the Royal Society, with Louis as the first author.
Collaboration in Turing now takes place on a large scale. For me, it has taken me into fruitful new areas, my collaborators making it both manageable for me and adding new skills – thereby solving the ’10,000 hours’ challenge!