As a young Ph.D. student, I thought that my thesis supervisors were annoying. Looking back, ten years later, I think they were not nearly harsh enough.

• I used to think that keeping detailed logs of what I have done was pedantic. As a young researcher or developer, I would just quickly jot down my ideas without looking back. I have since learned that this argument that seems so obvious to you now, may escape you a year later. You have to write a lot. All the time. As a side benefit, if you try to explain carefully what you just did, you often find out flaws faster. You also think better if you slow down.
• The little things do matter. I used to believe that science was about the big issues. I could not be bothered about small details. I was so wrong! Science is about being anal retentive over little details. This off-by-one result may hide a significant result, or may confuse an eventual reader. You have to try hard to get everything right as early as possible.
• Communication is 80% of the work. This may sound counterintuitive because most researchers only spend a small fraction of the time publishing or giving talks. But when they design experiments, or craft theorems, they are trying to make a point, to communicate an idea, to an imaginary peer. So, you have to design elegant experiments and theoretical results all the time. Hack all you want, but hack elegantly.

Scientists often cheat. Bad and famous scientists cheat. The cheating can be small or large: putting your name as an author on a paper that you barely read, omitting part of the an experiment, making up experimental results, claiming that you have a proof of a given result, making something look more complicated than it really is, and so on.

Cheating can serve you well. It may help you get a larger grant, a better job, and so on. However, all these gains are short term ones. For longer term goals, I believe cheating eventually makes you less relevant.

This idea came to me as I was reading a comment on this blog:

A scientist or mathematician may achieve relevance as a side-effect of aiming for rigour. (Peter Turney, somewhere on this blog)

Update: One of my colleague has written a book on scientific frauds (in French). Thanks to Sébastien Paquet for the link.

We are looking for someone to fill a permanent position as an education specialist (spécialiste en sciences de l’éducation). The job includes some research time. You must have a degree in education, or the equivalent. Some of our specialists have Ph.D.s. Some training in Computer Science would be great. The job location is Montreal and the language is French. If you are interested, do not get in touch with me, but send your resume:

Les personnes intéressées doivent faire parvenir leur curriculum vitae ainsi que leur(s) attestation(s) d’études avant 16 h 30, le 5 mai 2008 à la :
Direction des ressources humaines
À l’attention de madame Nathalie Camiré
Concours no. 0804-912
455, rue du Parvis
C.P. 4800, succ. Terminus
Québec (Québec)
G1K 9H5

Back when I was a Mathematics undergraduate student at the University of Toronto, I was told by some of my peers that I was not a Mathematician but a problem solver. This was meant as a derogatory remark, but I thought it was a correct assessment. In short, I cared only about a given theorem if it allowed me to solve some interesting problems. I was not interested in Mathematics for its own sake. Rigor was not enough, I wanted relevance.

A given scientific or mathematical results has two properties: rigor and relevance. You usually can have one, or the other, but not both.

Engineers and technologists are good at determining relevance. They will discard quickly results that they do not need. The average software engineer is unable to prove that his program is correct. Even when rigor is important, such as when designing medical gear, the engineer is often not interested in proving the optimality of the techniques being used. By sacrificing some rigor, the engineer is able to innovate: if he had to prove every detail, he could never get work done.

Scientists make a business out of correctness. To ensure rigor and depth simultaneously, scientists stay close to the shore. Most scientists specialize in a narrow niche and take months to study what might be considered to be a minor point. This same minor point will get revisited by others. Their work tend to be very incremental. However, scientists are bad at being critical of the revelance of their own work. Indeed, if they did question their work too often, they may need to change topic too often which would reduce considerably their productivity. This explains why we end up with fields such as String theory or classical AI. Notice that you cannot measure relevance by the number citations from people in your field. In fact, the relevance of one’s research is usually never formally measured.

You would think that being critical would be a good thing in science, no? Alas, no. As an experiment, try to go to the next conference in your field and ask your peers whether what you are doing is relevant. It is a good recipe to become unpopular.

References:

Aubrey D.N.J. de Grey, Curiosity Is Addictive, and This Is Not an Entirely Good Thing, Rejuvenation Research. February 1, 2008, 11(1): 1-3.

Dijkstra’s second rule for successful scientific research: “We all like our work to be socially relevant and scientifically sound. If we can find a topic satisfying both desires, we are lucky; if the two targets are in conflict with each other, let the requirement of scientific soundness prevail.”

This year, I am the course coordinator for a Java course. One of our tutors went missing. Human resources tried to negotiate with him but he told them he did not care anymore.

I googled him. I got his resume, and then noticed that the top line says “2008: now with Google.”

I guess that must be a common phenomenon in hot spots like Stanford? It was a first for me.