[My Computational Complexity Web Log] Finding Problems to Work On
Often graduate students ask me for a good problem to work on. This is one of the biggest challenges of an advisor. A good problem for a graduate student must fulfill each of three characteristics.
I generally give the advice that my advisor, Michael Sipser, gave to me. Pick up a proceedings of a recent conference in your area and read through the abstracts of papers until you find one that interests you. The "interests you" part is important, for without it you won't have the motivation to study further.
Read the paper thoroughly. Read related papers. If you lose interest, start the process all over again.
Once you've read several papers in an area that interests you talk about it with your advisor and your fellow graduate students. Some of these papers might list open questions and you could work on those. You might say, "Karp listed this as an open question, and if Karp can't solve it why should I be able to?" Karp is a very smart but also very busy person. It is unlikely he spent more than an hour thinking hard about these questions. As a graduate student you can spend much more time focusing on these problems and could easily make more progress than someone like Karp could.
Even better is to formulate your own problems. Perhaps there is an interesting variation in a model that the original paper, for whatever reason, did not cover. Perhaps you can find connections between two papers that no one had noticed before. These are great problems to work on: As you are breaking new ground, theorems can start flowing like water. Just remember not to have too much weirdness in your questions; keep the research interesting.
Posted by Lance Fortnow to My Computational Complexity Web Log at 4/14/2003 10:48:03 AM
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