Learn the Upstream

v0.1

It seems that learning the upstream of your research field would be very helpful for your research. This claim assumes that a field has a upstream field, or all fields are constructed in a hierarchical structure. I guess most people would agree with this. For example, the upstream field of Computer Science is mainly Mathematics. Mathematics provide language, tools and theorems to build the foundation of Computer Science, and many people (e.g. [1]) believe that the prerequisite of a good Computer Scientist is a solid knowledge of Mathematics. This article will expand this point to other fields by presenting several examples.

Most sub fields in computer security are more or less rooted in cryptography, which is no doubt the earliest sub field in computer security and the most rigorous one. This explains that several famous security researchers such as Ross Anderson and Bruce Schneier, started their career in cryptography, and then "invaded" many other sub fields. Andrew Yao might be another example. He switched from Physics to Computer Science, and got Turing Awards.

More examples can be found. Yin Wang has been criticizing many important products in computer science, such as SQL, Unix, Go language, ... While the validity of his criticisms are always in debate, I think they do have some value. And I further realize that it is because Yin Wang is from the programming language field, which is more or less the upstream of many other computer science subfields, such as database and OS. My roommate also serves an interesting example. Once a Math major undergraduate student, he switched to the field of Deep Learning now. Compared with researchers in CS background, his knowledge in Math helped him understand the problem deeper.

People always talk about jumping out of the box is the way towards creativity. Well I guess learning the upstream is the way towards the outside of the box. Isn't it?

References:

[1] The picture. http://www.nolandalla.com/wp-content/uploads/2014/02/salmon.jpg

[2] How to do Research At the MIT AI Lab. David Chapman. 1988