Gaston Gonnet (founder of the MAPLE computer algebra system) in an interview with Thomas Haigh in 2005 (SIAM History of Numerical Analysis and Scientific Computing Project; see here for the complete interview). I have fond memories of MAPLE as as a tremendously helpful tool for checking operations involving special functions, something that I dabbled in quite some time ago.
|Now that I have worked several years in bioinformatics, the work in bioinformatics can be summarized as: you have to be good at algorithms, and you have to be very good at probability and statistics. You are not working with completely deterministic objects. You are not working with mathematical formulas that go only one, you are not working with problems which have a unique and precise answer. You are working with nature that has gone into a process of evolution in a relatively random way. This randomness percolates everything that you do because this randomness is not only in nature, but in all the data that you acquire. You acquire data, and the data is not exact. It’s subject to error because of the nature of the data or the nature of the acquisition of the data.
What I tell all my students and my grad students when they come is to make sure that their background in algorithms and their background in probability and statistics are really strong. If they have a good background in algorithms and statistics, quite a bit of scientific computation helps. It helps if someone knows how to integrate a system of differential equations or finding a minimum in an efficient way. Those kinds of basic scientific computation abilities are also very helpful. But if you are good at those two and possibly that third one, you are going to be good a bioinformatician. There is no two ways about it. But you have to understand algorithms and statistics, and that’s maybe the crucial point.