Editor's note: Uwe Aickelin didn't train as a typical mathematician, nor biologist, yet he now finds himself at the heart of a project that mixes the two. While following his love for mathematics, he came across concepts inspired by the natural world and became hooked on these ideas.
I started out as an undergraduate student in business science at the University of Mannheim in Germany, my home country. This degree covers all aspects of business and management studies, including marketing, finance, law, economics, and production scheduling. I chose business science at the time because I wanted to study something worthwhile that would also lead to a well-paid job. However, my true passion had always been mathematics, so whenever I could, I picked options that were related to mathematical subjects. One such topic was operational research, which investigates how to model business situations mathematically in order to make the best decisions, subject to certain criteria and limited resources.
If It Works in Nature, Why Not in Business?
I was offered the chance to specialise in this area with a master's at the University of Wales, Swansea, during which I learnt about those algorithms that are borrowed from the sphere of artificial intelligence to solve the more complex problems. With one such algorithm, evolutionary computation, a number of random solutions to a problem are created and subsequently allowed to "breed," thereby creating more solutions. This, together with a Darwinian "survival of the fittest" amongst all solutions and an occasional "mutation," eventually leads to better and better solutions. I was hooked by this approach, as it seemed simple yet powerful. After all, if it works in nature, why not in business? This is a motto I would come back to again and again in my career.
So I decided to undertake a Ph.D. in Swansea to get to know these algorithms a bit better. I chose Kathryn Dowsland as my supervisor, both for her expertise in the area and as someone I thought I would get along with very well. I would strongly advise any prospective Ph.D. student to meet their supervisor a few times before starting, as a mutual understanding and respect is probably more important than the exact topic of the Ph.D. My own project was to use evolutionary algorithms to optimise solutions to multiple-choice problems, such as which nurse should be allocated to which shift in a hospital.
Coming to the end of my Ph.D. in 1999, I was still contemplating entering the world of business as a management consultant, but I had started to enjoy the world of research. In the end I decided on a lectureship in operational research in the School of Mathematics at the University of the West of England ( UWE) in Bristol.
When making this choice, I had to turn down a postdoc. Even though a lectureship is often seen as the Holy Grail for young researchers, I actually found the choice difficult. I knew that going for a lectureship straight away would be more of a challenge as well as more risky in the long run. Choosing the postdoc route may mean that you effectively enter the profession later, but it leaves you more space and time to build up a good publication record so that you might actually end up in a better position a few years down the line.
Still, I took the plunge and met my first challenge as a lecturer: teaching! Although I had taught the occasional lab class before and UWE was running support classes for new lecturers, I found my first year of teaching very difficult and time-consuming. But it was through my teaching that I first became interested in the biological concept--danger theory--that is behind my current project.
I gave my students an investigation of so-called artificial immune systems (AIS) as a piece of coursework. AIS are similar to evolutionary algorithms. However, rather than borrowing ideas from evolution and Darwin, they draw on concepts related to the human immune system.
One of my students, Steve Cayzer, who unbeknownst to me held a Ph.D. in biological sciences, did a much better job than my average student, and we found that we could devise AIS algorithms that were useful for solving complex data-mining problems. Consider the example of renting a video. To make sure that you pick a movie you have never seen and will like, what you really need are a couple of friends who know your tastes and have seen a wide range of films. But if your friends aren't around, you could always turn to an AIS for recommendations.
How? Well, your tastes would be mathematically modelled as "antigens" (or targets) and then matched against possible "antibodies," i.e., movies that have previously been entered into a database according to the likes and dislikes of other people. In the human immune system, the antibodies would kill the antigens, but in the AIS, they can be used to predict films you would like.
Responding to Danger Signals
This led me to ask immunologists around UWE what they thought of AIS. It turned out that they were not aware that immunology was a source of inspiration to mathematicians! After some interesting discussions I enquired about what was hot in immunology and was given a paper entitled "The Danger Theory." This theory challenges the traditional idea that the body attacks intruders because they are recognised as foreign to the body, arguing that what the immune system responds to is a signal of danger instead. I immediately saw the potential benefits of applying this theory to the design of an AIS that would provide an intrusion detection system (IDS) for computer security.
Although current AIS show great potential for tackling intruders, they have been held back by the huge amount of processing required by traditional models of natural immune systems. If you consider using the classical paradigm to protect a computer network against everything that is foreign to it, it means mapping the rest of the world! The danger theory provides brand-new ideas upon which to design AIS.
Through our danger theory project we are trying to construct effective artificial defence mechanisms that could be used for computer security as well as financial fraud detection and network fault management. The project got funded by the Engineering and Physical Sciences Research Council and is the largest of its type in the United Kingdom.
Writing the proposal was difficult, as it meant learning a lot of the immunological phrases and vocabulary. I was greatly helped by Julie McLeod, an immunologist at UWE, and by my student Steve Cayzer, who, knowing both sides of the coin, was an excellent translator. Finally, other experts I met at conferences and workshops--immunologists from the University of Bristol and IDS experts from University College London, Hewlett-Packard, and ECSC, a leading computer-security company--also got involved in the project. So my one piece of advice on writing proposals for multidisciplinary projects is to ask the experts. OK, it will take you some time and effort to understand what they are talking about, but it is much better than trying to do it yourself and second-guessing.
I now work at the University of Nottingham, where I am the principal investigator of the danger theory project, which means leading a team of 10. Hence I am busier than ever! The project will last for another 3 years, and I look forward to discovering more about using nature's ideas to solve our problems.
If you would like to find out more about Uwe Aickelin's work or get in touch with him, see his Web site. .