One additional thing I want to mention is, that the job of the supervisor is not only to guide the student to the PhD (i.e. the mentor role), but also to ensure that the student actually has done sufficient work to earn the PhD (the role of a gatekeeper). Which can lead to the situation, that the student thinks they should be graduating now, but the supervisor is still asking for more work. Now there are some supervisors that are abusing the gatekeeper role. Furthermore even for supervisors that are not consciously abusing the role there is also a conflict of interest, because it is in the supervisors interest to keep successful students as long as possible. So this is difficult to manage.
I think of my PhD students as my primary "product" I am creating as a professor. Not to say I don't think that teaching is valuable, but I'm by far proudest of my PhD students who graduate and go on to have fantastic careers. That said, I'm proud, but also sad, and worried because once they become good they leave. I let them depart once they have met our agreed upon goals, which are pretty much the bare minimum for being competitive for research-oriented jobs in industry (not "just a coder," but designing and doing independent research).
A relative of mine got his PhD with a prof who was kind of famous for such a document, and all of his students continued that tradition in their own labs. Of course you know the drill. Part of it is to influence your students, but the other part is to influence yourself.
Currently experiencing this weariness and my output isn't great. Covid restrictions + WFH are making it worse I feel but ultimately it's my responsibility to do well. Any tips for not looking like a hopeless case to your supervisor at this stage?
After we went to remote work, my students have been making weekly presentations to me, which serves to update me on their work and helps them become better communicators of their research.
They recap what I suggested, they give a status update on what I asked them to do and whatever else they did, they compare the results of their experiments to past experiments (baselines), and they propose next steps and I make suggestions with regard to what I think makes sense.
In my field, machine learning, we often stitch multiple papers together, so each project is aimed at being a paper in the dissertation. For my lab, we come up with the idealized main contributions for a paper, and then work toward those goals. We may not meet them, but it provides structure and their informal presentations to me facilitate organized discussion and analysis of results.
I come at this from a different field (the biomedical sciences), wherein it is rare to find clearly-articulated career goals from the PhD students, and even harder to find supervisors who care. This problem is made worse when supervisors often rely on their students' naivete to keep their labs staffed.
The students can be forgiven for this naivete: they often start their degrees with the assumption that, if they work hard, a tenure-track or similar position will be available to them at the other end. Of course, reality is that they may fail to achieve that goal no matter how hard they work, simply because they have the wrong project/wrong mentorship/poor funding/etc. The supervisors, on the other hand, are often quite happy for their students to remain naive, as the incentives of students and supervisors are often misaligned.
The solution is probably twofold:
First, it should be essential for prospective students to come in with a very clear idea of their career goals (e.g. "I want a tenure-track faculty position in a leading research university" or "I want a dedicated teaching position at the community college level").
Second, said prospective students should be given clear, objective advice, along the lines of your expectations document, on what it takes to achieve those goals. This advice should help guide students in choosing a lab that will help them achieve these career goals, and ideally should come from a neutral third-party. One can't expect supervisors to be unbiased in this regard.
What professors at research universities do:
1. Mentor PhD students (and often also MS and BS students)
2. Write papers and edit student papers
3. Write grant proposals
4. Deal with a lot of administrative issues
5. University service committees - some are on multiple some are on none (admissions, COVID response, undergraduate program, graduate program, etc.)
6. Review papers from others
7. Teach one or more courses
8. Develop new courses (takes 1-2 days to make a good lecture)
9. Update lecture materials (I spend probably 2 hours per lecture for courses I've already taught)
10. Meetings
11. Conference/Workshop organization
12. MANY miscellaneous duties
13. Budget management
14. Prepare talks
15. Serving on MS/PhD proposal/defense committees for students not in your lab
16. Staying on top of the scientific literature
17. Making homework assignments (some can delegate to TAs)
18. Grading (some can delegate)
19. Write letters of recommendation (I write around 10-15 per year)
Of course you can get this load down by learning to say "No" in some cases. But a professor during their first couple years when they start the job can easily expect to put in 60-80 hours per week. It gets a lot easier after that.
1st year: we had a 1 hour meeting every week
2nd year we had a 1 hour meeting every 2 weeks
3rd year (1t hslf) we had a 1 hour meeting every 2 weeks
3d year 2nd half onwards we had a 1 hour meeting if I emailed them and asked them for it
If I needed more time I could always knock on their door. And oftentimes we went to have lunch for informal meetings.
Now, i always felt my supervisors were absolutely amazing. But i quickly understood that to complete my PhD, i needed to scratch with my own hands and that my sups were there only to guide me.
All in all my experience was great. Nevertheless after finishing and doing 4 years as a postdoctoral i decided the "publish or perish" mentality was not for me, and moved to industry.
Also they are your advisors, not teachers. At PhD level, you are supposed to make a meaningful contribution to the discipline on your own.
Mentoring by more senior folks in the lab certainly helps everyone (https://www.pnas.org/content/116/42/20910), but it's barely measured or rewarded for PIs and not at all for postdocs. Changing this would be an easy fix though--ask for a reference from a junior colleague when hiring.
More generally, there's a huge disconnect and sudden between the skills needed to get a faculty job (i.e., publishing a high profile papers as an individual contributor) and the skills needed to do well at it (e.g., management). Everyone is all antsy to shorten postdocs, but I'd actually be in favor of making the career path more gradual with so people get some mentored management experience.
(sigh...)
1) 1 journal paper as the first author, so that they learn how that process (often is a review or perspective paper on their field, which goes in their dissertation)
2) 2 first author papers in top-tier machine learning venues (goes in dissertation)
3) 1 first author paper in a top-tier or second tier venue (goes in dissertation)
4) 1 collaborative paper with another PhD student (have to learn how to collaborate)
By the end, they have at a minimum around 4 first author papers and 1 additional paper. They can then turn these into their dissertation and these are signals to employers that they are competent scientists. If a student tells me they want to be a faculty member, we increase the numbers a bit (need at least 10 first-author papers to be competitive).
I also require that they be organized when we have our one-on-ones in terms of their experimental output.
That's pretty much it. It may sound like a lot, but I try to put training wheels on for the first couple projects and make them as tractable as possible because I fleshed out the project and found some low-hanging fruit, before I start trying to make them be more independent and drive the process.
In my stints around Denmark and Japan (Computational Chemistry) you basically need ~1 top-tier or ~3 mediocre papers during your 3-year PhD.
In the US, PhD students in Computer Science and similar fields usually require 5-7 years to finish. They typically do not have MS degrees, unlike in Europe, where the expected graduation time is 3-4 years and they must have MS degrees.
You would be surprised how often there is a lot of low-hanging fruit. I'm good at asking "easy" questions that nobody knows the answer to, so a student just has to put in the work to get the answer. Many advisors don't give as much initial hand holding as I do.
So far my PhD students have taken 3-6 years to finish. Those that have graduated so far have finished with 3 papers (3 years), 9 papers (3 years), and 12 papers (5.5 years). The senior ones still in my lab (year 4 or 5) are on track to have around 3 first author papers in top-venues and about 9 papers total each. Their first author papers are in CVPR, ICCV, ACL, ECCV, BMVC, NAACL, ICLR, AAAI, etc.
Let me break down the issues a bit on the education side, first. Almost always by year 4+ most PhD students are fatigued and starting to get very weary. I was definitely one of them, and at times I did feel like my supervisor wasn't helping my career development or giving me enough mentoring. He is a wonderful, kind, and creative person and he spent 1-2 hours per week with me, but for whatever reason I still felt this way.
I eventually decided I felt that way because he didn't make his expectations for me finishing and tailoring those expectations to my career goals clear up front, and very few advisors do so. The first thing I did when I became a professor was write up an expectations document, and when students want to join my lab I walk them through it, and even then we do a trial run if they still want to. I think this helps a lot. Each semester I review career goals, progress to graduation, etc. with a student, and we include things like publication targets, internships, etc., depending on career goals. I think of my PhD students as my apprentices, and it is my job to get them to be strong scientists and to help them achieve their career goals, as long as they put the work in. Once I became a professor I was much more understanding and sympathetic to my own advisor. Until you have the job, you don't really understand the challenges.
All that said, I've seen very bad advisors who exploit students, fail to mentor them well, make them stay far longer than needed (ambitious new faculty), neglect to push them hard enough to actually achieve their career ambitions (tenured faculty past their prime), and various sorts of emotional abuse.
Industry is no picnic, though. Depending on the job, you may have little autonomy (you don't get to decide what you do, unlike academia), be abused in terms of work hours (many start ups), subjected to micromanagement, and verbal abuse definitely does exist.
Take a look at "Ask A Manager" and you will hear many many tales of abuse and drama in industry: https://www.askamanager.org/