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There are plenty! But that's besides the point. You most likely will end up writing some software as part as a PhD in CS, but that's not the objective. Most of

There are plenty! But that's besides the point. You most likely will end up writing some software as part as a PhD in CS, but that's not the objective. Most of the time, the software will be proof-of-concept stuff to validate some of the research you've done. Or to assess the performance of a new algorithm, or whatever. Your comparison of equating R&D to hacking on some open-source project is really apples to oranges. In any case, a PhD in CS is, 99.9999% of the time, not about hacking, at all.

sedachv 16y ago
If a grad student writes a software prototype and no one uses it, does it make a sound? The fact that "99.9999% of the time" is spent doing other things is the problem. If you're doing applied math like type theory and make an advancement, that's research. If you're doing anything else, that's the Development part of R&D. Computer science doesn't exist in a vacuum. Unlike math, you can't come up with some new algorithm without first having a problem that that algorithm is intended to solve (again, this doesn't apply to complexity-related research because that is applied math). So we can't claim CS arises a priori (unless it's just applied math like type theory), so then you have to claim that what your CS research is doing is empirical. Good luck with that. Because, you know, Java is a naturally occurring system. Let's take two concrete examples of projects I hack on: http://common-lisp.net/project/eager-future/ http://common-lisp.net/project/eager-future/ http://common-lisp.net/project/parenscript/ http://common-lisp.net/project/parenscript/ Both of these topics (using futures for parallelism, and language X to JavaScript compilation) have had and continue to have a lot of academic research devoted to them. These two systems I contribute to happen to have interesting and original properties that I've arrived at in a process that can only be described as hacking. I'm convinced that I never would have been able to have these hacking breakthroughs without the freedom and time to reflect that I have outside of grad school. It doesn't matter how supportive your advisor is, there is still the question of grants and publishing deadlines. And I know a thing or two about working the university system to create a supportive environment for myself - as a math undergrad, I had my own cubicle in and run of the AI lab, had support from a number of CS professors, one of whom had a spot reserved in the CS MSc program for me (I obviously didn't go). I spent a lot of time around grad students at the AI and other CS labs, so I got to know what grad student life looked like. But aside from all that, the best thing about working on these systems on my own has been the fact that people actually use them. The feedback has been invaluable for giving me new ideas and inspiring the innovative hacks.
- tincholio 16y ago
  >The fact that "99.9999% of the time" is spent doing other things is the problem. If you're doing applied math like type theory and make an advancement, that's research. If you're doing anything else, that's the Development part of R&D. It's not a problem, at all. Whether you're developing new algorithms, analyzing them, running simulations, studying and understanding the results, all that _is_ research. I think you're being too narrow in your definitions.

sedachv 16y ago

If a grad student writes a software prototype and no one uses it, does it make a sound? The fact that "99.9999% of the time" is spent doing other things is the problem. If you're doing applied math like type theory and make an advancement, that's research. If you're doing anything else, that's the Development part of R&D. Computer science doesn't exist in a vacuum. Unlike math, you can't come up with some new algorithm without first having a problem that that algorithm is intended to solve (again, this doesn't apply to complexity-related research because that is applied math). So we can't claim CS arises a priori (unless it's just applied math like type theory), so then you have to claim that what your CS research is doing is empirical. Good luck with that. Because, you know, Java is a naturally occurring system. Let's take two concrete examples of projects I hack on: http://common-lisp.net/project/eager-future/ http://common-lisp.net/project/eager-future/ http://common-lisp.net/project/parenscript/ http://common-lisp.net/project/parenscript/ Both of these topics (using futures for parallelism, and language X to JavaScript compilation) have had and continue to have a lot of academic research devoted to them. These two systems I contribute to happen to have interesting and original properties that I've arrived at in a process that can only be described as hacking. I'm convinced that I never would have been able to have these hacking breakthroughs without the freedom and time to reflect that I have outside of grad school. It doesn't matter how supportive your advisor is, there is still the question of grants and publishing deadlines. And I know a thing or two about working the university system to create a supportive environment for myself - as a math undergrad, I had my own cubicle in and run of the AI lab, had support from a number of CS professors, one of whom had a spot reserved in the CS MSc program for me (I obviously didn't go). I spent a lot of time around grad students at the AI and other CS labs, so I got to know what grad student life looked like. But aside from all that, the best thing about working on these systems on my own has been the fact that people actually use them. The feedback has been invaluable for giving me new ideas and inspiring the innovative hacks.

tincholio 16y ago
>The fact that "99.9999% of the time" is spent doing other things is the problem. If you're doing applied math like type theory and make an advancement, that's research. If you're doing anything else, that's the Development part of R&D. It's not a problem, at all. Whether you're developing new algorithms, analyzing them, running simulations, studying and understanding the results, all that _is_ research. I think you're being too narrow in your definitions.