This article is written by the holy blogging trinity of the School of Computing at the University of Utah: Matt Might, John Regehr, and myself. If you don't read their blogs, you should, because you'll learn how to program robots using an iphone interface in subzero weather.
There have been a lot of Ph.D.-bashing articles lately. There have been some spirited defenses of a Ph.D. too. Most of these articles make good observations, but they're often about the larger Ph.D. ecosystem and therefore fail to provide actionable advice to (potential) Ph.D. students.
We observe that most failures of the Ph.D. system -- including both failure to get the degree and failure to see a good return on time and money invested in obtaining the degree -- boil down to a small set of root causes. These causes are on both sides of the implicit contract between advisor and advisee. Here's our pragmatic view of the conditions that need to be met for a Ph.D. to make sense. (Please keep in mind that we're all computer science professors, though we've made an effort to avoid field-specificity.)
The advisor shall...
1. Advise the student: help find a thesis topic, teach how to do research, write papers, give talks, etc.
2. Provide protection from and information about funding concerns (to the level of expectations of the field, which vary widely).
3. Proactively provide realistic, honest advice about post-Ph.D. career prospects.
4. Provide early and clear guidance about the time frames and conditions for graduation.
5. Introduce the student to the academic community, through conference talks, invited talks, letters of recommendation, etc.
The student shall...
1. As early as possible, do due diligence in researching career prospects. It's not hard to get people to talk about this and there's also plenty of written advice, in books and on the web. Carefully filter what you read since the situations may be very different between engineering fields, science fields, and the humanities. There
may also be significant differences between sub-fields such as theoretical computer science vs. operating systems. A new student should glance at job postings and NSF statistics to determine the ratio of new Ph.D.s to open tenure-track slots.
2. As early as possible, determine if the actual career prospects are a reasonable match for their needs/expectations. Until the student makes her expectations clear, the advisor has no clue if she simply
must have an academic job or whether he'll be perfectly happy getting a Ph.D. and then going to law school or being a stay-at-home parent.
3. Not be deluded or blinded by catchphrases like "life of the mind." Indeed, this life does exist, but probably only during the ABD portion of a Ph.D. A professor would be extremely lucky to live the life of the mind 15 hours a week, leaving 60 hours of advising, teaching, reviewing, writing grant proposals, traveling, and sitting in meetings.
4. Be a good investment in terms of time and money. In other words, work hard. Students who periodically disappear for long bouts of skiing, soul searching, or contract work tend to be put on the back burner by their advisor, making it much more difficult to get re-engaged later on. An easy litmus test: if acting a certain way
would get you fired from a real job, then it's probably a bad idea to try that in a Ph.D. program too.
5. Jump through the administrative hoops appropriately. The hurdles are important and generally not too burdensome: take some classes, do a qualifying exam, write a proposal, and so on. These are easy to
ignore until they become a problem. Your advisor is not likely to remind you, or even remember that you need to do them.
Since nothing is obvious on the internet, a disclaimer: These edicts might come across as cold and overly pragmatic, and might suggest that we are ignoring the joy of discovery, the thrill of learning and the excitement of doing cutting-edge research that goes along with doing a Ph.D. Far from it: we've chosen this life because we experience all of this and enjoy it. But the easiest way to crash and burn in what is a long, multi-year haul is to forget about the brass tacks and float in the clouds.
Suresh, what would your advise be to a student switching to CS from, say, electrical engineering. I did that transition and at times I feel attracted to study courses that are not related to my research; Robotics and abstract Math for example. I see so many online courses these days ( www [dot] coursera [dot] org ) and I feel tempted to study all of it.
ReplyDeleteyou need to be very judicious in what you study. There's an ocean of fascinating work out there, and you can't ever hopet o learn all of it.
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