This one in particular had me temporarily cut off contact with people who could not be bothered to remember that I had no interest in answering this question!

Also, random catalysmic events, like in year 4 your advisor accepts a job at a different university in another state.

It's less good advice in fields where grad student research output doesn't matter as much, and where students do more teaching instead. Those fields tend to make much more aggressive use of weed-out exams to ensure that they have enough young grad students to meet teaching demand but not so many older (>=3yr) grad students that they saturate advising capacity. Mathematics in particular comes to mind.

nearly half of my year didn't get this and had to master out when we got to quals.

Or failed quals?

In many programs, the department aims to admit far more people than will pass the quals. They need the Calculus and Pre-calculus TAs but do not have the advising capacity.

Even if everyone gets a 95% on the quals, the majority will "fail" by necessity because the department simply does not have the advising capacity for the number of TAs they need. Of course, the department typically designs the quals to these needs either explicitly or implicitly.

This is usually at least implicitly understood by the faculty, who will navigate it when absolutely necessary. For example, I've seen it happen that if a professor really needs a student and vouches for/protects them (eg because the research is computational and the student came from 5 years at Google), then the student gets more goes at the plate on quals than is typical.

However, we were a small department and your acceptance was predicated on at least one faculty member wanting you to join their lab. Some people moved around within their first year, but the majority stayed with the first group they joined.

His logic is that when you are doing research, you are pushing the envelope into new territory that can't be taught in a classroom. When you are in a classroom you are learning old material that is already well-known and established.

This is very true in CS. But far from true in Math and Physics where there probably is a lot of advanced learning available in classes. The few classes I had that he actually endorsed being "worth your time" were Math classes focused around encryption (of which I took 3 different ones).

But my advisor was unique because he was 100% there for the research. He only taught because the university forced him to. He lived and breathed research and that was the only reason he was in academia. He was truly passionate and worked 10+ hours a day on research, but thats why he was there. He had a very low opinion of classroom teaching.

If they do not make the grading lenient - it’s for a reason.

At least in the poor (and honestly mostly useless) parts of Mathematics. Maybe Physics is less poor.

(Fortunately I was in CS, where the research output is actually needed by society and usually not pure masturbation, so the attitude toward coursework was "do well at what you need, enjoy what you want, and ignore what you don't need or want"

Then again, knowing that that @£&$€¥ would drop you might make it a good plan.

In contrast, if you come in mostly ready to go and these classes are just refreshers, you can spend time in that class working on actual research and impressing the prof as well as not panicking if/when you realize you don't understand what's going on.

He e-mailed me right before the year ended saying he had changed his mind and didn't want any more grad students, basically dumping me.

Right around the same time, my first backup decided to retire.

My second backup passed away.

I was left no longer making "sufficient progress" and no path to do so, losing my financial aid.

Of course there's plenty of additional ways to derail this as well, including advisor moving, advisor getting into a fight with the rest of the department, advisor giving poor advice, advisor deciding that they don't like you, etc.

If the advisors can vouch for, or strike a student regardless of their qual performance - then why not simply have an end of year performance review?

On the other hand, not all departments are good fits with students and there's a very wide asymmetry in information between many new students and programs, even if you "do your research" beforehand, given just how specialized these disciplines are at a high level. It would be nice if transferring programs was made easier and if more departments would just agree to help students "master out" and look for jobs rather than discard them like roadkill.

In my program we had annual committee reviews as well as a review submitted to the program chair and graduate school of the student by their advisor (with the student providing both a self-review and review of their advisor). Ultimately, unless you’re in a sub-field with many faculty, it is hard to get an accurate evaluation from three professors. My other committee members could understand the big picture of my work but they weren’t experts on the specifics, and they were the best equipped faculty in the department to be on my committee besides my advisor. The goal is to make sure that there is a paper trail and multiple professors aware of your progress (or lack thereof), so your advisor can’t just give you the boot for something tertiary like not watching his dog during a holiday weekend.

Professors are aware of their problematic peers in the department. Even if they can’t fire them outright (tenure has pros and cons), they can steer students away from them to more supportive professors (or give you a hint that maybe you should consider a different school during your visit day). Our program chair was very good at helping relocating students who initially started in the lab of one or two bad actors.

At some places, advisors now also consider a list of students ranked by grades. I have seen advisorship being offered only to the top student in the Professor's class.

If you lack ex-ante information how and where your grade rank matters in a year, this just adds a fun new challenge to the first year PhD!

This is one of the things I thought of right away when ChatGPT got released last year. "God, there's probably so many PhD candidates right now in NLP feeling despair like all their work was pointless ...as if million of voices cried out in terror and were suddenly silenced."

It's hard in the moment to know whether what you're working on has any utility. So just do your best and keep chugging!

PhD is granted for novelty, not practicality.

And this attitude, my friends, is the reason why so much software out there is so bad.

We need more of a math mindset when developing software. What can we be sure about, what are the invariants, what can we prove? There is so much crap out there that somebody lacking understanding just tried to wing, and I'm constantly ashamed of it.

Computer science is applied math.

Foundational research does often not provide immediate applications. Still, if we don't do it, out understanding of the world is lacking and it hurts us later down the road.

Math has no application until you find an application for it. Differential equations are just equations until you pair them with physics. Formal logic is just an abstract discussion of human reasoning until you build a circuit, etc.

A slight equivalent I can think about would be the emergence of neural networks. When I was working on my Masters on face recognition, neural networks were not the major force they are now. Facial landmarks used a combination of haar features and edge detection. These methods weren't outright abandoned, but if NNs had taken off during my research, then I would have had to restart my work.

any advice for people aiming for teaching instead of all the publishing stuff?

As to what to do during your PhD: find an advisor that is happy to have one or two students focused on teaching and outreach (they would like to have that because when applying for grants it makes it easier for them to explain how they have broader impact, pointing to your work).

Not completely true. UC Berkeley at least has tenure-track lecturers, now apparently mostly referred to as "Teaching Professors" (https://apo.berkeley.edu/sites/default/files/teaching_profes...)

A random (old) job post for this: https://gsso.ce.gatech.edu/2022/01/12/tenure-track-teaching-...

> Thankfully, many universities (even the prestigious ones) are now starting to hire more semi-permanent teaching-focused staff (and some even use the title professor for these).

Even if you are an awesome teacher, you are going to be required to continue publishing a minimum amount every few years and you will be hired based on what you published.

Sorry, but that's just academia. If you want to teach without doing research, then maybe look at Community Colleges, High Schools, or getting a job at a corporate job and being an Adjunct Professor for 1-2 classes a semester.

During the PhD, I was a TA and instructor on record for several classes. Schools may have some form of mentor teaching assistantship that lets you get experience teaching while in the program. I think I taught ~6 courses by the time I graduated.

It helps to position yourself its to the "education" research space for your field. There is a strong CS education research space, so you can incorporate your classroom as your "lab", though you'll want to study up on Cognitive Sciences to ensure your findings support current literature. My publication count is much lower than my peers, but I was still able to receive several offers for teaching faculty positions.

Teaching faculty positions are available, though not in as much demand as traditional research oriented profs. However, I know at least in CS there are several universities looking for them. Likewise, by situating yourself in the education space, you can land research prof while still focusing on education. If you get funding, then you can buy out course obligations so you can specialize in teaching a single class.

The point about the lack of opportunities for advancement/moving due to the course preps and teaching taking up your time is very true. While your friends at R1s are on pre-tenure sabbaticals, getting course buyouts, and teaching a nice grad seminar for a semester, you might be doing 3-4 new preps a year and likely getting piled with service work.

Oh ... I felt that one :-/

Then give you the option a year later to congratulate him on the startup’s multi-billion exit.

In the game it pretty much continously went up.

n) Your old college friends have secured their material needs while you barely make rent.

n) a PhD student that joined the program after you just surpassed your number of publications.

n) your thesis supervisor just bumped you off primary author to contributor in your own paper.

I mean, woah. Even that's a little too far for phd-land