8 ms·

How to do a PhD

79 points by efz1005 11y ago

rdlecler1 11y ago
Definitely need to focus. I think most of us come into our PhDs thinking that our research is going to change the world. For anyone doing highly theoretical work we can be over ambitious. We try to bite off more than we can chew, and then we're either paralyzed or we spend a good portion of our time back peddling to simpler problems. If I could give one piece of advice. Start simple. If possible replicate some of the earlier work because you may discover that you're building on a lot of unreported assumptions. Aim for a portfolio of small papers that together tell a larger story, rather than trying to pack it all into on opus magnus.
- amelius 11y ago
  > For anyone doing highly theoretical work we can be over ambitious. I would say that this danger applies more to those doing practical (applied) work. A practically oriented program will often have a clear goal, and preset goals are often unattainable in research. By contrast, a student doing theoretical work can always bend their goals when necessary.
  - Aeolos 11y ago
    That is very true in my experience.
  - ska 11y ago
    I think this contributes to why theoretical degrees take, on average, much less time to complete in my experience.
- rodion 11y ago
  > If possible replicate some of the earlier work because you may discover that you're building on a lot of unreported assumptions. I concur wholeheartedly, and this is my recommendation to every new PhD student. Replicating earlier work has many benefits for a PhD student in the beginning of their studies: * It forces them to deeply understand the system they're replicating. Much more deeply than simply reading the paper would. * It allows them to uncover the many corner cases which the original paper probably just briefly mentions. (If it mentions them at all.) * It makes them think about how they would design an experiment so that it is repeatable and reproducible. * It is a good source for inspiration for alternative solutions to the problem or even new problems altogether. Alas, replication is way too uncommon in systems CS, which I think is one of the reasons why other scientists often look down on systems research.
rubidium 11y ago
While it has a bit of snark to it, certainly conveys the point of "This is your PhD. No one is going to hold your hand. Get it done." The people who drop out of PhD programs usually are plenty smart enough, they just don't know how to make things happen. Then again, some people seem content and happy to do 8 years of PhD studies followed by 10 more of meandering post-docs. Bummer is when they're surprised that no one wants to make them a professor.
- drjesusphd 11y ago
  > they just don't know how to make things happen I don't think that's fair either.
- ska 11y ago
  The people who drop out of PhD programs usually are plenty smart enough, they just don't know how to make things happen. Vast oversimplification. I've watched people leave Ph.D programs for a wide array of reasons. Any attempt at binary classification on this is foolhardy.
  - static_noise 11y ago
    Clone a person ten times and send them to ten different positions, they will all fail for different reasons. Luck is as big a factor as is talent and diligence.
- jackcosgrove 11y ago
  I think most PhD students are set up to fail by graduate institutions. Graduate programs can often be corrosive environments with unrealistic expectations. Moreover even if you run the gauntlet and get a tenure-track job or a position in a national or even corporate lab, the training you received in graduate school is often inadequate preparation for the next step. I am not ascribing this latter point to malice but rather to a self-perpetuating canon that becomes increasingly distant from real-world applications. Universities also exploit people who depend on academic success for self-worth and validation. The head games that advisors play to goad graduate students on to do more work can be pretty unscrupulous. The very fact that universities can convince some of the smartest (if not those most possessing in common sense) people to work long hours for below minimum wage, while learning a body of knowledge of dubious value, is pretty amazing when I look back at it. In the real world, people pay you good money to do PhD level work. Job security and low work loads aren't on offer if you do an expected value calculation factoring in all of the tenure washouts. The only thing graduate programs offer in return for collapsing highly educated wages is an infinitesimal chance at fame. It is no less an exploitative championship system than professional sports or Hollywood. I think the only good reason to seek a PhD is for altruistic reasons, to expand human knowledge. But keep in mind that you are choosing the life of a 21st century cleric, with its attendant material deprivation and very good chance of never marrying or having a family.
cranium 11y ago
While the article is interesting, I find it quite confusing to read with its paragraphs once ironic once advisory...
cwmma 11y ago
11 commandments ...
ideonexus 11y ago
5. Get to know the literature I'm not writing a thesis, but a book on teaching coding, and this one point is really important. One of my chapters was on the cognitive benefits of writing software code--which I assumed were great because of Seymor Papert ("Mindstorms") and Ted Nelson's ("Computer Lib/Dream Machines") glowing enthusiasm for teaching kids how to code. I started writing the chapter and had to get into the literature to find references to support the idea that learning to code carried concrete cognitive benefits. The research was extremely mixed, with one survey of the literature being highly critical of Papert for assuming so many benefits when the research was not finding that at all. I ended up having to stop everything and just read papers for a week to understand what science really knew about the subject. When I went back to rewrite my chapter, I had to temper my own enthusiasm and add numerous qualifications and cautions about my claims considering the evidence. I'm happy for the experience as my thoughts on the subject are much more highly nuanced now, but it was very disheartening at first.
- Quanticles 11y ago
  There are pro's and con's to studying the literature. The main pro is that you can avoid a dead end. The main con is that your ideas will be shaped by the literature (i.e., not as unique). I tend to try to solve a problem myself first (this could be days or weeks), then look at the literature. If I looked at the literature first, then my ideas would look like the literature. It's a gamble either way
- ArkyBeagle 11y ago
  CS literature is uniformly abysmal.
  - kctess5 11y ago
    False.
- Obi_Juan_Kenobi 11y ago
  Beyond that, you have to talk to people. At least in my field, there is an uncomfortable amount of cultural knowledge; many papers are known to be bunk, but have not been retracted, or are otherwise compromised. You wouldn't know it from reading the literature but for a few carefully worded sentences when referencing some results. I maintain that the most important domain knowledge is understanding what evidence/methods are actually robust, and what just sounds nice. But yes, any research requires a lot of reading. If you don't like doing that (and ideally you really enjoy it) you're going to have a very tough time of it. You also won't have the background to have good conversations with people or to design experiments.
stared 11y ago
I am after my PhD and I consider quality of this list being close to of a "be creative" list. That is, in principle all points make sense, but they are either truism, too obvious, too vague or things that are out of control. (If I had read it before starting my PhD it wouldn't have changed a thing). Or maybe I am overly skeptical of simplistic life-advice? Is there any take-home message that changed your way of acting?
- jackcosgrove 11y ago
  Here is a piece of advice for CS graduate students: take a look at the Gartner hype cycle. Here's the cycle from 2014: http://www.gartner.com/newsroom/id/2819918 http://www.gartner.com/newsroom/id/2819918. Pick something far on the left side of the curve, and ride the hype.
  - nmrm2 11y ago
    I'm not so sure this is great advice for PhD students. Junior faculty maybe. You should fix a lot of other things first (in particular, prefered methodology and advisor) and then, after fixing those things, choose applications toward the left-hand side. A few reasons: 1. If you're doing it for hype alone you will be miserable. Ph.D. students tend to be out ahead of pretty much everyone in terms of the nitty gritty details, so they often hit the trough of disillusionment right around the time everyone else is at the top of the hype curve. 2. A Ph.D. is a once-in-a-lifetime opportunity to be paid (sometimes comfortably) to work on (mostly) whatever you want. If it's not at least a little self-indulgent, you're wasting a great opportunity. 3. You'll have the rest of your life to chase the next sexy thing.
  - Xcelerate 11y ago
    "Quantum computing" is ahead of "connected home"?
    - nmrm2 11y ago
      These curves are marketing material for an "advisory firm". Nothing more, nothing less. The idea of the curbe is more important that the particular points, and the points are often mis-placed. Also, lots of technologies jump around randomly on the curve or move rapidly through some parts, etc.
marincounty 11y ago
I really wish they would just get rid of the PhD. Keep the Master's degree. Have a hard series of tests, that if a person passed, they would get, I don't know, maybe a "Certified Master in Whatever."? The test would be difficult--maybe like the CA bar, NY bar? Harder! It would save a lot of money. It would save a lot confusion about a person's abilities. Right now, I look at a fresh, young PhD with a lot of skeptism. Actually, I just fiqure it's a rich kid who wanted to stay in school. If the PhD is gotten later in life, and the thesis is a actually original, I admire the extra effort. Why did I bring this up. Some of the worst Instructors, and insufferable people have come across have been PhD's. And in pretty much every field the Master's degreed candidate knows just as much as the PhD, and you put them in a room, and the there's a lot of laughter over the the whole Dr. thing. Plus, I'm tired of calling someone a Doctor who isn't a MD. Actually these days, with extreme specialization; lets get rid of Dr.? In my world, a Doctor who didn't specialize is an experienced nurse, or PA. I had a girlfriend whom killed so many animals for her thesis. Literally hundreds. She would tell me these stories in bed--until I told her "Enough--I just don't want to know." It's was all to get that title. She wanted that title so bad. Did you know in the the bio sciences, after some wannabe researcher tests his/her drug/procedure on an animal, the animal is bashed over the head, so when the pathology/histology/whatever is performed---the brain/tissue is not contaminated in any way? They couldn't even use CO2, or Helium to kill the animal. At the time the students were going to lab using tunnels around the school, so they would have to look at the protestors--at least that's what she told me. She told me she felt empowered killing the animals at the time. Why--because the reward would be great. Being called a Dr! I never quite looked at her the same way after these forced conversations. I would wake up in that cold, damp, marina apartment, with all her goth artwork hanging around the bed; and sometimes I thought she was a actually a witch. She worked very hard at the whole witch thing at the time. All that doesn't matter. I was just astonished at the cruelty that went on in those labs, all so the students could get that PhD. I once asked her about the "original research" that goes on. She laughed hysterically. I was so wrong she pointed out. She then caught herself, and said "Well, maybe someone was doing something original?" Well, after the money ran out, and the realization she was no scientist, she moved to San Francisco and partied through the late 90's/2000's. She is now stuck with a lot of debt. She is also a vegan. Well, she's not quite a vegan. She eats Salmon for her dry hair. Not quite a vegan? And yes, I got an earful, when I brought that up. I was just tired of her telling everyone, "I'm a vegan!" She further told people she was a PhD. I knew, and she knew, but wanted that title so bad, even when she didn't get it she would tell people she was a PhD. The funny thing is she isn't crazy. She actually gets paid pretty well, but thankfully, not in her field of study, with her magical PhD. She was beautiful though. I remember thinking, why am I with her? I didn't think I was a shallow person. I guess I am.
henrik_w 11y ago
Or, as in my case, don't do a PhD. I really thought I wanted to, but as it turns out, I didn't: http://henrikwarne.com/2016/03/07/ph-d-or-professional-programmer/ http://henrikwarne.com/2016/03/07/ph-d-or-professional-progr...
jboggan 11y ago
If you get the first one wrong the rest of the list will not matter at all. The most important questions are, who will my advisor be? What is the completion rate for graduate students under their care? What kind of personal network does the advisor maintain and what kinds of roles do the graduating PhDs go into? I wish I had known to ask that sort of thing. My advisor typically kept a lab of a dozen postdocs and a single PhD at any given time. I think in 25 years of being a research professor with sizeable grants that advisor only graduated 4 doctoral students, and a rather distressing proportion of the postdocs left not only academia but science after that lab. I would also try to hang out with the current doctoral students and assess their psychological well-being.
- redraga 11y ago
  While this is completely valid advice, it might not be applicable with junior faculty who may not have graduated many students. I chose my advisor (who was as assistant professor at the time) based on the belief that I'll get to publish aggressively since it's in both of our interests. But I had no way of knowing how hard it would be to work with her. And I wasn't the only person who felt this way; I saw my fellow group mates (all of us joined the group at around the same time) and other collaborators express similar sentiments in course of time. But since I'd already sunk in time, I decided to stick around. I was able to successfully complete my PhD, but I took more time than expected. Moreover, I was no way near as productive as I'd hoped to.
- kxr 11y ago
  ^ A thousand times this. Vet your advisor like there's no tomorrow. This is trickier than it sounds because the best source on this is your advisor's current or past grad students, and they often won't open up cause they don't know you. Your filter should then be: "if they don't give you great/enthusiastic feedback about their advisor, play it safe and look elsewhere."
- Obi_Juan_Kenobi 11y ago
  It is the most important factor, but I disagree that it's that easy to tell (if that's part of what you're implying). Some people can actually do very well in what appear to be hostile labs. Some needs lots of space, while others do best with close monitoring. It ultimately just comes down to whether the advising relationship works or not, and it's nearly as complicated as romantic relationships. One thing that I like to stress is that you don't have to like your advisor. You should probably not dislike them, but what matters is how you play off each other and work together. An example: some advisors will put the people in their lab in somewhat adversarial positions. There will be two or more people working on similar projects, and the more successful of them will start to get more and more of it. For certain types, this situation is actually very motivating and can produce two differentiated projects that support each other. For others, it will undermine their confidence and wellbeing to a terrible degree. There can be a lot of resentment among both parties. While I would generally consider this a warning sign, you must recognize that this can be a good environment for the right person. The difficulty is that it's hard for you to know what works for you until you do it. There's no best formula, you just have to choose as best you can.
graycat 11y ago
I've seen a lot of really good people get very badly hurt pursuing a Ph.D. I did get a STEM field Ph.D. but didn't get hurt. For a good and broad view of the problem, buried in D. Knuth's The TeXBook is "The traditional way is to put off all creative aspects until the last part of graduate school. For seventeen or more years, a student is taught examsmanship, then suddenly after passing enough exams in graduate school he's told to do something original." Yes, here Knuth identifies a significant challenge. Compared with the OP, here's a very different and much more specific approach that clearly makes a lot of sense and that worked well for me: First, note carefully that for some work that can be called research the usual, nearly universal criteria are that the work be "new, correct, and significant". Below, keep these three in mind. Second, get a major in math, at least a good undergraduate major in pure and applied math and hopefully enough more in pure and applied math for roughly a Master's in math. Even if you don't care about the Master's degree, I do very much recommend getting the math for a Master's degree. Why pure math? The pure math gives you the crucial, central, foundational tools of math, that is, many crucial prerequisites and, broadly, the ability to state and prove theorems. E.g., you will learn how to write math, and that alone will start to put you ahead, even of some high end professors. What pure math? For your research likely mostly you will use the part of math called analysis but in your studies for more you will also want at least the basics of abstract algebra and maybe differential geometry, combinatorics, and maybe even some in foundations. In addition, if you have some reason to believe you can get some value from algebraic topology or geometry, then, sure study those. Why applied math? Likely applied math will be closer to the math you will use for your research. What applied math? Sure, e.g., statistics, numerical linear algebra, ordinary differential equations, more in numerical techniques, optimization, stochastic processes, etc. Third, get your Ph.D. in some field of engineering -- computer science, electronic engineering, mechanical engineering, civil engineering, operations research, statistics, etc. Three biggie points: (1) In science and engineering, by far the most highly respected research is that which mathematizes the field. Good work here can help meet the criterion of "significant". (2) Work in math, well supported with theorems and proofs, is much more difficult to criticize than work that is mostly just experimental or empirical. Good work here can be help meet the criterion of "correct". (3) The standard and severe weakness of the backgrounds of researchers in most of science and in engineering is way too little in math. Thus, there are a lot of good research problems they can't address. So, your good work here can be help meet the criterion of "new". So, with your background in math, on (1)-(3) you will have at least a good -- maybe even an overwhelmingly strong -- comparative, competitive advantage. Another point if you care: Unless your family wants to donate $10+ million or so, it is just super tough to get into an Ivy League university. But getting in as a grad student is much easier -- e.g., I got accepted to Cornell, Brown, and Princeton. So, you should intend that your research be essentially math for that field of engineering. Usually you will aim to use your math tools to solve a relatively practical problem in that field of engineering, but you might use your math to add to the basic theory of that field; for some wild guesses, you might do something in the theory of predators and prey in environmental engineering; maybe you would have been the one who did Kalman filtering in electronic engineering; maybe in mechanical engineering and continuum mechanics you will make some nice theoretical contribution to materials science. Why engineering instead of pure math or physical science? (1) Engineering has no end of practical problems -- say, from outside of, and neglected by, academics -- to be solved. So, if you pick, attack, and solve a problem important in practice, then there is a good chance your work will meet the criterion of "new", since the work is mathematical, "correct", and since the problem was important in practice, "significant". (2) In pure math and physical science, the range of candidate problems is much more narrow, e.g., in physics you can try to say what dark energy is -- lots of luck doing that. So, right, for a research problem in some field of engineering, maybe pick a practical problem that is considered important and that you found someplace, maybe outside academics, maybe on a job, maybe a real job or maybe just a summer job or an internship. I did that: I picked a problem I found at FedEx. Then, it will be quite good for you to have the problem in mind when go for your Ph.D. I had the problem and a good, first-cut, intuitive solution (worked out on an airplane flight) before I entered my Ph.D. program. In my first year, I took some advanced, relatively pure, not often taught, graduate math coursework that gave me good math prerequisites to let me convert my intuitive solution a solid math solution. So, in my first summer, in six weeks, independently, alone in the library, I worked out the math, with theorems and proofs, and walked out with a 50 page manuscript that was the original research for my Ph.D. dissertation. I recommend doing such a thing. Getting into research early is commonly considered good advice: E.g., IIRC, the Princeton math department has said on their Web site that a student should have some research underway in their first year. Even, better, have the core research done before the second year -- which is what I did and, I believe, a strong advantage in getting the Ph.D. The math gave me another advantage: In a course, a problem was apparent -- a tricky, deep question about the Kuhn-Tucker conditions. There was no answer in the course, and I could find no answer in the library. So, I attacked the problem -- the key was some pure math I had -- and found a surprisingly nice solution, in two weeks. I wrote up my solution and got credit for a reading course. But the work was publishable -- presto, bingo, at that university the criteria for a Ph.D. dissertation was that the work be "an original contribution to knowledge worthy of publication". Well, the best way to show that some work is "worthy of publication" is to submit it for publication and have it accepted. I did that. So, technically that work was enough for my Ph.D. dissertation, a second one. For that problem in the Kuhn-Tucker conditions and for my dissertation research, I never had any real faculty direction. I recommend: Don't wait for the faculty to provide a good problem or direction. Instead, on your own as much as you can, at least if it is easy for you, and it was for me, pick a good problem, do the research, get the work ready for publication, and, hopefully, publish it. For a graduate student to have, early on, from largely independent effort, some work worthy of publication makes essentially everything else in the Ph.D. program and the start of a career much easier and better. Okay, how to do the research? Well, for me, the core, hard work of the research was a little more involved but, really, not much more difficult than the more difficult exercises in standard, advanced pure math texts. The difference was, for research, in part need to keep in mind some view from higher up, say, 50,000 feet down to 1000 feet and don't always be crawling around on the ground with the lowest level details (which is common and usually effective enough in solving exercises). Next, guess: To find and prove a new result, first have to guess it. Sure, make educated guesses based on your solid background but also work just intuitively. So build intuitive models and, as you learn more, revise the models to make them more accurate. E.g., during the work, is A true? Well, it doesn't seem wrong right away intuitively. But, if A is true, then, hmm, B is true. Could B be true? At least, first-cut, intuitively, naw, not a chance (this may be wrong, but let that happen for now). So, likely A is not true. You can do a lot of this in your head without writing anything. And, even if slowly, you will learn to do at least some derivations in your head. Now, for C, intuitively it looks true. So, try to prove C. Gee, the proof doesn't work. Then observe: The proof doesn't make good use of all the hypotheses of C; so, you've been trying to prove something more general than C and likely not true. Bummer. So look again at the hypotheses of C and try to see how they are essential and how to exploit them. So, continue in this way, maintaining a good view from above the ground level, with lots of intuition and guessing and trying to prove some little things. When you get a proof of a result that looks good, then write it up, carefully, cleanly, put a date and title on the first page, put a staple in the UL corner of the sheets, and toss it on a stack, continue on, maybe building on what you have. There is also Polya, How to Solve It. From A. Wiles, the guy who solved Fermat's last theorem and just won the Abel Prize, is "Perhaps I could best describe my experience of doing mathematics in terms of entering a dark mansion. You go into the first room and it's dark, completely dark. You stumble around, bumping into the furniture. Gradually, you learn where each piece of furniture is. And finally, after six months or so, you find the light switch and turn it on. Suddenly it's all illuminated and you can see exactly where you were. Then you go into the next dark room ..."
scott_s 11y ago
Be selfish. Finishing a PhD is unlike completing a project in most other jobs. In most other jobs, someone needs what you're working on. Other people's investment in the outcome is similar to your own. If you fail to complete your work, others are likely to fail to complete their work. Consequently, incentives (hopefully) line up, and infrastructure (hopefully) exists to support you, with the recognition that your success is linked to group success. Your PhD dissertation is not like this. Yes, your adviser is invested in you finishing - but not as nearly as much as you are. They will have other students, and they can always work on their own. Your peers may be invested, if they are working on another part of the project - but if you do not finish, they will find a way to get on without you. Your university is invested in you (quite literally, most of the time, with money), but again, not as nearly as invested as you are: plenty of grad students never finish, and they will help you, but schools also recognize that not all students finish. The author has a good list, and I may read his book, but he's missing this attitude that I felt I had to adopt. The person who cares most about you finishing is you, and sometimes that means having to be selfish in order to finish. That can take of the form of not engaging in as much service in your department, or not providing some help on a project that is not part of your dissertation. I do think this attitude is unfortunate, but it is a natural result of the requirement that a PhD dissertation represents work that the student owns. I much more enjoy the research I have done in an industry research lab, where me and my colleagues have collaborated equally. (Or equal-enough that in a grad school context, no one person could claim ownership of the work for a dissertation.) But, it's the system we have, and because of that, I think that in order to finish, grad students have to - at least eventually - adopt a selfish attitude. Specifically, this "selfish attitude" means ruthless evaluating: will this thing get me closer to graduation? If no, don't do it. (Obviously this only applies to work. Having a life outside of grad school work is enormously important.) In the beginning, I don't think one needs to have this attitude. But as you approach completion, I think one needs to start thinking this way.

79 points by efz1005 11y ago

Definitely need to focus. I think most of us come into our PhDs thinking that our research is going to change the world. For anyone doing highly theoretical work we can be over ambitious. We try to bite off more than we can chew, and then we're either paralyzed or we spend a good portion of our time back peddling to simpler problems. If I could give one piece of advice. Start simple. If possible replicate some of the earlier work because you may discover that you're building on a lot of unreported assumptions. Aim for a portfolio of small papers that together tell a larger story, rather than trying to pack it all into on opus magnus.

amelius 11y ago
> For anyone doing highly theoretical work we can be over ambitious. I would say that this danger applies more to those doing practical (applied) work. A practically oriented program will often have a clear goal, and preset goals are often unattainable in research. By contrast, a student doing theoretical work can always bend their goals when necessary.
- Aeolos 11y ago
  That is very true in my experience.
- ska 11y ago
  I think this contributes to why theoretical degrees take, on average, much less time to complete in my experience.
rodion 11y ago
> If possible replicate some of the earlier work because you may discover that you're building on a lot of unreported assumptions. I concur wholeheartedly, and this is my recommendation to every new PhD student. Replicating earlier work has many benefits for a PhD student in the beginning of their studies: * It forces them to deeply understand the system they're replicating. Much more deeply than simply reading the paper would. * It allows them to uncover the many corner cases which the original paper probably just briefly mentions. (If it mentions them at all.) * It makes them think about how they would design an experiment so that it is repeatable and reproducible. * It is a good source for inspiration for alternative solutions to the problem or even new problems altogether. Alas, replication is way too uncommon in systems CS, which I think is one of the reasons why other scientists often look down on systems research.

rubidium 11y ago

While it has a bit of snark to it, certainly conveys the point of "This is your PhD. No one is going to hold your hand. Get it done." The people who drop out of PhD programs usually are plenty smart enough, they just don't know how to make things happen. Then again, some people seem content and happy to do 8 years of PhD studies followed by 10 more of meandering post-docs. Bummer is when they're surprised that no one wants to make them a professor.

drjesusphd 11y ago
> they just don't know how to make things happen I don't think that's fair either.
ska 11y ago
The people who drop out of PhD programs usually are plenty smart enough, they just don't know how to make things happen. Vast oversimplification. I've watched people leave Ph.D programs for a wide array of reasons. Any attempt at binary classification on this is foolhardy.
- static_noise 11y ago
  Clone a person ten times and send them to ten different positions, they will all fail for different reasons. Luck is as big a factor as is talent and diligence.
jackcosgrove 11y ago
I think most PhD students are set up to fail by graduate institutions. Graduate programs can often be corrosive environments with unrealistic expectations. Moreover even if you run the gauntlet and get a tenure-track job or a position in a national or even corporate lab, the training you received in graduate school is often inadequate preparation for the next step. I am not ascribing this latter point to malice but rather to a self-perpetuating canon that becomes increasingly distant from real-world applications. Universities also exploit people who depend on academic success for self-worth and validation. The head games that advisors play to goad graduate students on to do more work can be pretty unscrupulous. The very fact that universities can convince some of the smartest (if not those most possessing in common sense) people to work long hours for below minimum wage, while learning a body of knowledge of dubious value, is pretty amazing when I look back at it. In the real world, people pay you good money to do PhD level work. Job security and low work loads aren't on offer if you do an expected value calculation factoring in all of the tenure washouts. The only thing graduate programs offer in return for collapsing highly educated wages is an infinitesimal chance at fame. It is no less an exploitative championship system than professional sports or Hollywood. I think the only good reason to seek a PhD is for altruistic reasons, to expand human knowledge. But keep in mind that you are choosing the life of a 21st century cleric, with its attendant material deprivation and very good chance of never marrying or having a family.

cranium 11y ago

While the article is interesting, I find it quite confusing to read with its paragraphs once ironic once advisory...

cwmma 11y ago

11 commandments ...

ideonexus 11y ago

5. Get to know the literature I'm not writing a thesis, but a book on teaching coding, and this one point is really important. One of my chapters was on the cognitive benefits of writing software code--which I assumed were great because of Seymor Papert ("Mindstorms") and Ted Nelson's ("Computer Lib/Dream Machines") glowing enthusiasm for teaching kids how to code. I started writing the chapter and had to get into the literature to find references to support the idea that learning to code carried concrete cognitive benefits. The research was extremely mixed, with one survey of the literature being highly critical of Papert for assuming so many benefits when the research was not finding that at all. I ended up having to stop everything and just read papers for a week to understand what science really knew about the subject. When I went back to rewrite my chapter, I had to temper my own enthusiasm and add numerous qualifications and cautions about my claims considering the evidence. I'm happy for the experience as my thoughts on the subject are much more highly nuanced now, but it was very disheartening at first.

Quanticles 11y ago
There are pro's and con's to studying the literature. The main pro is that you can avoid a dead end. The main con is that your ideas will be shaped by the literature (i.e., not as unique). I tend to try to solve a problem myself first (this could be days or weeks), then look at the literature. If I looked at the literature first, then my ideas would look like the literature. It's a gamble either way
ArkyBeagle 11y ago
CS literature is uniformly abysmal.
- kctess5 11y ago
  False.
Obi_Juan_Kenobi 11y ago
Beyond that, you have to talk to people. At least in my field, there is an uncomfortable amount of cultural knowledge; many papers are known to be bunk, but have not been retracted, or are otherwise compromised. You wouldn't know it from reading the literature but for a few carefully worded sentences when referencing some results. I maintain that the most important domain knowledge is understanding what evidence/methods are actually robust, and what just sounds nice. But yes, any research requires a lot of reading. If you don't like doing that (and ideally you really enjoy it) you're going to have a very tough time of it. You also won't have the background to have good conversations with people or to design experiments.

stared 11y ago

I am after my PhD and I consider quality of this list being close to of a "be creative" list. That is, in principle all points make sense, but they are either truism, too obvious, too vague or things that are out of control. (If I had read it before starting my PhD it wouldn't have changed a thing). Or maybe I am overly skeptical of simplistic life-advice? Is there any take-home message that changed your way of acting?

jackcosgrove 11y ago
Here is a piece of advice for CS graduate students: take a look at the Gartner hype cycle. Here's the cycle from 2014: http://www.gartner.com/newsroom/id/2819918 http://www.gartner.com/newsroom/id/2819918. Pick something far on the left side of the curve, and ride the hype.
- nmrm2 11y ago
  I'm not so sure this is great advice for PhD students. Junior faculty maybe. You should fix a lot of other things first (in particular, prefered methodology and advisor) and then, after fixing those things, choose applications toward the left-hand side. A few reasons: 1. If you're doing it for hype alone you will be miserable. Ph.D. students tend to be out ahead of pretty much everyone in terms of the nitty gritty details, so they often hit the trough of disillusionment right around the time everyone else is at the top of the hype curve. 2. A Ph.D. is a once-in-a-lifetime opportunity to be paid (sometimes comfortably) to work on (mostly) whatever you want. If it's not at least a little self-indulgent, you're wasting a great opportunity. 3. You'll have the rest of your life to chase the next sexy thing.
- Xcelerate 11y ago
  "Quantum computing" is ahead of "connected home"?
  - nmrm2 11y ago
    These curves are marketing material for an "advisory firm". Nothing more, nothing less. The idea of the curbe is more important that the particular points, and the points are often mis-placed. Also, lots of technologies jump around randomly on the curve or move rapidly through some parts, etc.

marincounty 11y ago

I really wish they would just get rid of the PhD. Keep the Master's degree. Have a hard series of tests, that if a person passed, they would get, I don't know, maybe a "Certified Master in Whatever."? The test would be difficult--maybe like the CA bar, NY bar? Harder! It would save a lot of money. It would save a lot confusion about a person's abilities. Right now, I look at a fresh, young PhD with a lot of skeptism. Actually, I just fiqure it's a rich kid who wanted to stay in school. If the PhD is gotten later in life, and the thesis is a actually original, I admire the extra effort. Why did I bring this up. Some of the worst Instructors, and insufferable people have come across have been PhD's. And in pretty much every field the Master's degreed candidate knows just as much as the PhD, and you put them in a room, and the there's a lot of laughter over the the whole Dr. thing. Plus, I'm tired of calling someone a Doctor who isn't a MD. Actually these days, with extreme specialization; lets get rid of Dr.? In my world, a Doctor who didn't specialize is an experienced nurse, or PA. I had a girlfriend whom killed so many animals for her thesis. Literally hundreds. She would tell me these stories in bed--until I told her "Enough--I just don't want to know." It's was all to get that title. She wanted that title so bad. Did you know in the the bio sciences, after some wannabe researcher tests his/her drug/procedure on an animal, the animal is bashed over the head, so when the pathology/histology/whatever is performed---the brain/tissue is not contaminated in any way? They couldn't even use CO2, or Helium to kill the animal. At the time the students were going to lab using tunnels around the school, so they would have to look at the protestors--at least that's what she told me. She told me she felt empowered killing the animals at the time. Why--because the reward would be great. Being called a Dr! I never quite looked at her the same way after these forced conversations. I would wake up in that cold, damp, marina apartment, with all her goth artwork hanging around the bed; and sometimes I thought she was a actually a witch. She worked very hard at the whole witch thing at the time. All that doesn't matter. I was just astonished at the cruelty that went on in those labs, all so the students could get that PhD. I once asked her about the "original research" that goes on. She laughed hysterically. I was so wrong she pointed out. She then caught herself, and said "Well, maybe someone was doing something original?" Well, after the money ran out, and the realization she was no scientist, she moved to San Francisco and partied through the late 90's/2000's. She is now stuck with a lot of debt. She is also a vegan. Well, she's not quite a vegan. She eats Salmon for her dry hair. Not quite a vegan? And yes, I got an earful, when I brought that up. I was just tired of her telling everyone, "I'm a vegan!" She further told people she was a PhD. I knew, and she knew, but wanted that title so bad, even when she didn't get it she would tell people she was a PhD. The funny thing is she isn't crazy. She actually gets paid pretty well, but thankfully, not in her field of study, with her magical PhD. She was beautiful though. I remember thinking, why am I with her? I didn't think I was a shallow person. I guess I am.

henrik_w 11y ago

Or, as in my case, don't do a PhD. I really thought I wanted to, but as it turns out, I didn't: http://henrikwarne.com/2016/03/07/ph-d-or-professional-programmer/ http://henrikwarne.com/2016/03/07/ph-d-or-professional-progr...

jboggan 11y ago

If you get the first one wrong the rest of the list will not matter at all. The most important questions are, who will my advisor be? What is the completion rate for graduate students under their care? What kind of personal network does the advisor maintain and what kinds of roles do the graduating PhDs go into? I wish I had known to ask that sort of thing. My advisor typically kept a lab of a dozen postdocs and a single PhD at any given time. I think in 25 years of being a research professor with sizeable grants that advisor only graduated 4 doctoral students, and a rather distressing proportion of the postdocs left not only academia but science after that lab. I would also try to hang out with the current doctoral students and assess their psychological well-being.

redraga 11y ago
While this is completely valid advice, it might not be applicable with junior faculty who may not have graduated many students. I chose my advisor (who was as assistant professor at the time) based on the belief that I'll get to publish aggressively since it's in both of our interests. But I had no way of knowing how hard it would be to work with her. And I wasn't the only person who felt this way; I saw my fellow group mates (all of us joined the group at around the same time) and other collaborators express similar sentiments in course of time. But since I'd already sunk in time, I decided to stick around. I was able to successfully complete my PhD, but I took more time than expected. Moreover, I was no way near as productive as I'd hoped to.
kxr 11y ago
^ A thousand times this. Vet your advisor like there's no tomorrow. This is trickier than it sounds because the best source on this is your advisor's current or past grad students, and they often won't open up cause they don't know you. Your filter should then be: "if they don't give you great/enthusiastic feedback about their advisor, play it safe and look elsewhere."
Obi_Juan_Kenobi 11y ago
It is the most important factor, but I disagree that it's that easy to tell (if that's part of what you're implying). Some people can actually do very well in what appear to be hostile labs. Some needs lots of space, while others do best with close monitoring. It ultimately just comes down to whether the advising relationship works or not, and it's nearly as complicated as romantic relationships. One thing that I like to stress is that you don't have to like your advisor. You should probably not dislike them, but what matters is how you play off each other and work together. An example: some advisors will put the people in their lab in somewhat adversarial positions. There will be two or more people working on similar projects, and the more successful of them will start to get more and more of it. For certain types, this situation is actually very motivating and can produce two differentiated projects that support each other. For others, it will undermine their confidence and wellbeing to a terrible degree. There can be a lot of resentment among both parties. While I would generally consider this a warning sign, you must recognize that this can be a good environment for the right person. The difficulty is that it's hard for you to know what works for you until you do it. There's no best formula, you just have to choose as best you can.

graycat 11y ago

I've seen a lot of really good people get very badly hurt pursuing a Ph.D. I did get a STEM field Ph.D. but didn't get hurt. For a good and broad view of the problem, buried in D. Knuth's The TeXBook is "The traditional way is to put off all creative aspects until the last part of graduate school. For seventeen or more years, a student is taught examsmanship, then suddenly after passing enough exams in graduate school he's told to do something original." Yes, here Knuth identifies a significant challenge. Compared with the OP, here's a very different and much more specific approach that clearly makes a lot of sense and that worked well for me: First, note carefully that for some work that can be called research the usual, nearly universal criteria are that the work be "new, correct, and significant". Below, keep these three in mind. Second, get a major in math, at least a good undergraduate major in pure and applied math and hopefully enough more in pure and applied math for roughly a Master's in math. Even if you don't care about the Master's degree, I do very much recommend getting the math for a Master's degree. Why pure math? The pure math gives you the crucial, central, foundational tools of math, that is, many crucial prerequisites and, broadly, the ability to state and prove theorems. E.g., you will learn how to write math, and that alone will start to put you ahead, even of some high end professors. What pure math? For your research likely mostly you will use the part of math called analysis but in your studies for more you will also want at least the basics of abstract algebra and maybe differential geometry, combinatorics, and maybe even some in foundations. In addition, if you have some reason to believe you can get some value from algebraic topology or geometry, then, sure study those. Why applied math? Likely applied math will be closer to the math you will use for your research. What applied math? Sure, e.g., statistics, numerical linear algebra, ordinary differential equations, more in numerical techniques, optimization, stochastic processes, etc. Third, get your Ph.D. in some field of engineering -- computer science, electronic engineering, mechanical engineering, civil engineering, operations research, statistics, etc. Three biggie points: (1) In science and engineering, by far the most highly respected research is that which mathematizes the field. Good work here can help meet the criterion of "significant". (2) Work in math, well supported with theorems and proofs, is much more difficult to criticize than work that is mostly just experimental or empirical. Good work here can be help meet the criterion of "correct". (3) The standard and severe weakness of the backgrounds of researchers in most of science and in engineering is way too little in math. Thus, there are a lot of good research problems they can't address. So, your good work here can be help meet the criterion of "new". So, with your background in math, on (1)-(3) you will have at least a good -- maybe even an overwhelmingly strong -- comparative, competitive advantage. Another point if you care: Unless your family wants to donate $10+ million or so, it is just super tough to get into an Ivy League university. But getting in as a grad student is much easier -- e.g., I got accepted to Cornell, Brown, and Princeton. So, you should intend that your research be essentially math for that field of engineering. Usually you will aim to use your math tools to solve a relatively practical problem in that field of engineering, but you might use your math to add to the basic theory of that field; for some wild guesses, you might do something in the theory of predators and prey in environmental engineering; maybe you would have been the one who did Kalman filtering in electronic engineering; maybe in mechanical engineering and continuum mechanics you will make some nice theoretical contribution to materials science. Why engineering instead of pure math or physical science? (1) Engineering has no end of practical problems -- say, from outside of, and neglected by, academics -- to be solved. So, if you pick, attack, and solve a problem important in practice, then there is a good chance your work will meet the criterion of "new", since the work is mathematical, "correct", and since the problem was important in practice, "significant". (2) In pure math and physical science, the range of candidate problems is much more narrow, e.g., in physics you can try to say what dark energy is -- lots of luck doing that. So, right, for a research problem in some field of engineering, maybe pick a practical problem that is considered important and that you found someplace, maybe outside academics, maybe on a job, maybe a real job or maybe just a summer job or an internship. I did that: I picked a problem I found at FedEx. Then, it will be quite good for you to have the problem in mind when go for your Ph.D. I had the problem and a good, first-cut, intuitive solution (worked out on an airplane flight) before I entered my Ph.D. program. In my first year, I took some advanced, relatively pure, not often taught, graduate math coursework that gave me good math prerequisites to let me convert my intuitive solution a solid math solution. So, in my first summer, in six weeks, independently, alone in the library, I worked out the math, with theorems and proofs, and walked out with a 50 page manuscript that was the original research for my Ph.D. dissertation. I recommend doing such a thing. Getting into research early is commonly considered good advice: E.g., IIRC, the Princeton math department has said on their Web site that a student should have some research underway in their first year. Even, better, have the core research done before the second year -- which is what I did and, I believe, a strong advantage in getting the Ph.D. The math gave me another advantage: In a course, a problem was apparent -- a tricky, deep question about the Kuhn-Tucker conditions. There was no answer in the course, and I could find no answer in the library. So, I attacked the problem -- the key was some pure math I had -- and found a surprisingly nice solution, in two weeks. I wrote up my solution and got credit for a reading course. But the work was publishable -- presto, bingo, at that university the criteria for a Ph.D. dissertation was that the work be "an original contribution to knowledge worthy of publication". Well, the best way to show that some work is "worthy of publication" is to submit it for publication and have it accepted. I did that. So, technically that work was enough for my Ph.D. dissertation, a second one. For that problem in the Kuhn-Tucker conditions and for my dissertation research, I never had any real faculty direction. I recommend: Don't wait for the faculty to provide a good problem or direction. Instead, on your own as much as you can, at least if it is easy for you, and it was for me, pick a good problem, do the research, get the work ready for publication, and, hopefully, publish it. For a graduate student to have, early on, from largely independent effort, some work worthy of publication makes essentially everything else in the Ph.D. program and the start of a career much easier and better. Okay, how to do the research? Well, for me, the core, hard work of the research was a little more involved but, really, not much more difficult than the more difficult exercises in standard, advanced pure math texts. The difference was, for research, in part need to keep in mind some view from higher up, say, 50,000 feet down to 1000 feet and don't always be crawling around on the ground with the lowest level details (which is common and usually effective enough in solving exercises). Next, guess: To find and prove a new result, first have to guess it. Sure, make educated guesses based on your solid background but also work just intuitively. So build intuitive models and, as you learn more, revise the models to make them more accurate. E.g., during the work, is A true? Well, it doesn't seem wrong right away intuitively. But, if A is true, then, hmm, B is true. Could B be true? At least, first-cut, intuitively, naw, not a chance (this may be wrong, but let that happen for now). So, likely A is not true. You can do a lot of this in your head without writing anything. And, even if slowly, you will learn to do at least some derivations in your head. Now, for C, intuitively it looks true. So, try to prove C. Gee, the proof doesn't work. Then observe: The proof doesn't make good use of all the hypotheses of C; so, you've been trying to prove something more general than C and likely not true. Bummer. So look again at the hypotheses of C and try to see how they are essential and how to exploit them. So, continue in this way, maintaining a good view from above the ground level, with lots of intuition and guessing and trying to prove some little things. When you get a proof of a result that looks good, then write it up, carefully, cleanly, put a date and title on the first page, put a staple in the UL corner of the sheets, and toss it on a stack, continue on, maybe building on what you have. There is also Polya, How to Solve It. From A. Wiles, the guy who solved Fermat's last theorem and just won the Abel Prize, is "Perhaps I could best describe my experience of doing mathematics in terms of entering a dark mansion. You go into the first room and it's dark, completely dark. You stumble around, bumping into the furniture. Gradually, you learn where each piece of furniture is. And finally, after six months or so, you find the light switch and turn it on. Suddenly it's all illuminated and you can see exactly where you were. Then you go into the next dark room ..."

scott_s 11y ago

Be selfish. Finishing a PhD is unlike completing a project in most other jobs. In most other jobs, someone needs what you're working on. Other people's investment in the outcome is similar to your own. If you fail to complete your work, others are likely to fail to complete their work. Consequently, incentives (hopefully) line up, and infrastructure (hopefully) exists to support you, with the recognition that your success is linked to group success. Your PhD dissertation is not like this. Yes, your adviser is invested in you finishing - but not as nearly as much as you are. They will have other students, and they can always work on their own. Your peers may be invested, if they are working on another part of the project - but if you do not finish, they will find a way to get on without you. Your university is invested in you (quite literally, most of the time, with money), but again, not as nearly as invested as you are: plenty of grad students never finish, and they will help you, but schools also recognize that not all students finish. The author has a good list, and I may read his book, but he's missing this attitude that I felt I had to adopt. The person who cares most about you finishing is you, and sometimes that means having to be selfish in order to finish. That can take of the form of not engaging in as much service in your department, or not providing some help on a project that is not part of your dissertation. I do think this attitude is unfortunate, but it is a natural result of the requirement that a PhD dissertation represents work that the student owns. I much more enjoy the research I have done in an industry research lab, where me and my colleagues have collaborated equally. (Or equal-enough that in a grad school context, no one person could claim ownership of the work for a dissertation.) But, it's the system we have, and because of that, I think that in order to finish, grad students have to - at least eventually - adopt a selfish attitude. Specifically, this "selfish attitude" means ruthless evaluating: will this thing get me closer to graduation? If no, don't do it. (Obviously this only applies to work. Having a life outside of grad school work is enormously important.) In the beginning, I don't think one needs to have this attitude. But as you approach completion, I think one needs to start thinking this way.