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Written by Jason Eisner in 1997, for new Computer Science Ph.D. students at the University of Pennsylvania. He was a grad student himself at the time.

How to Find Research Problems

by Jason Eisner

The biological anthropologist Loren Eiseley used to say there were two kinds of scientists: big-bone hunters and small-bone hunters. (He himself was a small-bone hunter, he said, fitting little bits of data into the skeleton. If Eiseley had been a programmer, he would have called this "bottom-up science.")

Computer science includes many different kinds of research efforts, some of which are more tyrannosaurical than others. You can contribute to one of these efforts in various ways.

  • About the smallest bone that you can find in Computer Science is a replication or implementation of someone else's work.

    While this doesn't get you points for originality, it may be useful, both to your education and to the field. If you can make it useful to enough people (say, by making it portable and Web-available), it might even get your name known.


  • A significant small bone to look for is a tweak that improves a well-known technique. (In many subfields, you will be expected to demonstrate objectively that your method is an improvement.) Much research is of this kind.

    When reading papers, stay on the lookout for such bones. In particular, notice when the author may be making harmful simplifications or arbitrary choices in his/her approach. These are opportunities for you to try something different.


  • Along the same lines, you might make a controlled comparison of two or more algorithms, evaluating them by some objective measure of efficiency or accuracy. Designing a clean comparison does take thought, and carrying it out is often a lot of work.

    This is usually a medium-sized bone, depending on how much work it takes and (more important) how surprising the establishment finds your results. Note that quantitative studies of this sort are becoming increasingly important in some areas of CIS (e.g., operating systems, machine learning, natural language, algorithms).


  • You can thoroughly review the existing research in some area. Note that this takes a good deal of time to do well, and is not likely to do much for your career unless a lot of people read and cite your lit review. (To publish you'd typically need to co-author with a famous advisor, or else find some decent journal that is willing to publish high-level overview articles by lowly grad students.)

    On the upside, writing a lit review will make you something of an expert, able to talk confidently with other researchers in the area; it will give you an idea of the shortcomings of past research; and it may suffice for a WPE II, an M.S.E. thesis, or the first part of a Ph.D. thesis. You can make it available to others via your Web page or an online paper archive.


  • Build a large program or device of some kind. This gets you some name recognition, since there aren't that many big systems out there, and it also confirms your ability as a software engineer. However, do consider carefully: Will this system be of direct use to anyone? If not, will it at least beat performance records? If again not, does it have other merits, such as demonstrating how to integrate or scale up existing techniques, or introducing a collection of new techniques or a new perspective?

    If you are only one of many participants in a lab project, be sure that you make a ``separable contribution'' -- some piece of the work that is impressive, that stands alone, and that people will associate you with.


  • Your field identifies various problems or issues as significant. These often represent big bones in the skeleton of the field -- problems that arise often, and whose solution makes a difference. Get to know some of these problems and the work that's been done on them. If you see how to achieve the first-ever solution, or a better solution, or a different style of solution, that's a big deal. Sometimes finding a good solution involves changing the problem slightly.


  • If you are feeling ambitious and have a big-bone temperament, study important papers in your branch of computer science, flip through some conference proceedings to see what people are working on, and ask: What problems (recognized or unrecognized) are obstructing progress in my field? Can I solve them? If not, can I at least formalize them? Can I prove to my colleagues that solving them would make a difference?


  • Talk to your advisor about problems that are ripe for the plucking. Every field has its share of problems that everyone knows are ``kinda important,'' and that may even get mentioned a lot, but on which no one has yet made a serious attempt. If you think you spot such a problem, use your colleagues and the library to make sure it hasn't been plucked yet.


  • Finally, you can identify new interesting problems. This is often not as hard as it might sound:

    • Study existing (applied) systems and note what they do badly at.


    • If your field is interdisciplinary, ask people in the other discipline what they think is interesting. In fact, ask them why they think computer scientists are irrelevant.


    • In many areas, the data have a way of suggesting their own problems. Linguists can find unexplained phenomena in any magazine article. Systems programmers can collect data on actual disk access patterns and study it for regularities to exploit. Theoreticians of programming languages can look at real programming languages, and graphics programmers can look at real photographs and movies, for effects that they don't know how to capture.


Keep in mind: There's lots of research out there, so you have a choice about what to work on. (Even if your advisor is very hands-on, you still have some choice.) So, especially when you are considering a time-consuming project, keep your long-term goals in mind. Will it:

  • educate you?
  • lead to even better projects?
  • be an enjoyable way to spend your time?
  • serve a goal that will still seem worthy 6, 12, or 48 months from now?
  • be likely to "succeed" in some sense? (guideline: will it make an interesting conference talk?)
  • escape your advisor's imprecations?
  • get the academic research community interested in you and your work?
  • prove to an industrial employer that you have what they want?
  • make you a so-called ``famous grad student''?
Finally: Now that you're in grad school and no one sets your agenda, everything you do is open-ended. That means you can easily spend too much time on any task you start, especially if stubborn perfectionism or an inferiority complex leads you to feel that your work is never good enough, or if you're subconsciously trying to put off that scary next phase of your research.

  • Don't spend eternity on background reading. Recognize that you will have to start your work in a state of partial ignorance: you don't have time to learn everything you need to know. That's okay -- your professors do the same thing. In fact it's good, since ignorance leaves your mind free to see new ways of doing things. So start doing your own thinking early. You can alternate that with reading: just show your ideas periodically to someone who can warn you about related work and point you to relevant papers.


  • Don't spend eternity on one problem. No solution is ever complete. Take the time to make your work solid and beautiful and presentable, but recognize when you've hit a point of diminishing returns. Use project #1 to inspire project #2, which stands as research on its own. Don't use it as the core of project #1', #1'', etc. forever.


This page online: http://cs.jhu.edu/~jason/advice/how-to-find-research-problems.html
Jason Eisner - jason@cs.jhu.edu (suggestions welcome) Last Mod $Date: 2006/10/17 01:50:52 $
posted on 2010-10-26 20:09 baby-fly 阅读(272) 评论(0)  编辑 收藏 引用 所属分类:

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