This was a good post overall, I just have one modification.
Your advisor is the most important choice you can make. Talk to as many people as possible in the lab before you join it. If you and your advisor do not get along, your experience will be terrible.
I received this advice, and things worked out for me, but it’s dangerously incomplete. It is true that you need a good relationship with an advisor, and their recommendation letter matters when you’re on the job market. But for many areas the prestige of the department and university is more important. Put simply: you should probably go to the most prestigious PhD program that will take you. See this for example: “Across disciplines, we find that faculty hiring follows a common and steeply hierarchical structure that reflects profound social inequality.” Prestige is especially important if you want an academic position.
That’s a good point, prestige is very important. I would argue having a good relationship with your advisor is the most important, since its a bad idea to be in an abusive relationship for multiple years, but I will edit the main post to take this perspective into account!
Thank you for the thoughtful writeup! I knew very little about PhD programs before coming across this post—now I feel like I have the lay of the land :)
It seems like the GRE isn’t a dealbreaker for getting a PhD, but for anyone interested in 80⁄20′ing their GRE prep I’ll shamelessly bump my GRE advice post (thanks to Robi Rahman for his help)!
A few thoughts on ML/AI safety which may or may not generalize:
You should read successful candidates’ SOPs to get a sense of style, level of detail, and content c.f. 1, 2, 3.
Ask current EA PhDs for feedback on your statement.
Probably avoid writing a statement focused on an AI safety/EA idea which is not in the ML mainstream e.g. IDA, mesa-optimization, etc.
If you have multiple research ideas, considering writing more than one (i.e. tailored) SOP and submit the SOP which is most relevant to faculty at each university.
Look at groups’ pages to get a sense of the qualification distribution for successful applicants, this is a better way to calibrate where to apply than looking at rankings IMO. This is also a good way to calibrate how much experience you’re expected to have pre-PhD. My impression is that in many ML programs it is very difficult to get in directly out of undergraduate if you do not have an exceptional track-record e.g. top publications, or Putnam high scores etc.
For interviews, bringing up concrete ideas on next steps for a professor’s paper is probably very helpful.
My vague impression is that financial security and depression are less relevant than in other fields here, as you can probably find job opportunities partway through if either becomes problematic. Would be interested to hear disagreement.
A bunch of this definitely does generalize, especially:
“If you have multiple research ideas, considering writing more than one (i.e. tailored) SOP and submit the SOP which is most relevant to faculty at each university.”
“Look at groups’ pages to get a sense of the qualification distribution for successful applicants, this is a better way to calibrate where to apply than looking at rankings IMO. This is also a good way to calibrate how much experience you’re expected to have pre-PhD.”
And if you can pull this off, you’ll make an excellent impression: “For interviews, bringing up concrete ideas on next steps for a professor’s paper is probably very helpful.”
CS majors and any program that’s business relevant (e.g. Operations Research and Financial Engineering) have excellent earning/job prospects if they decide to leave partway through. I think the major hurdle to leaving partway through is psychological?
This was a good post overall, I just have one modification.
I received this advice, and things worked out for me, but it’s dangerously incomplete. It is true that you need a good relationship with an advisor, and their recommendation letter matters when you’re on the job market. But for many areas the prestige of the department and university is more important. Put simply: you should probably go to the most prestigious PhD program that will take you. See this for example: “Across disciplines, we find that faculty hiring follows a common and steeply hierarchical structure that reflects profound social inequality.” Prestige is especially important if you want an academic position.
That’s a good point, prestige is very important. I would argue having a good relationship with your advisor is the most important, since its a bad idea to be in an abusive relationship for multiple years, but I will edit the main post to take this perspective into account!
Thank you for the thoughtful writeup! I knew very little about PhD programs before coming across this post—now I feel like I have the lay of the land :)
It seems like the GRE isn’t a dealbreaker for getting a PhD, but for anyone interested in 80⁄20′ing their GRE prep I’ll shamelessly bump my GRE advice post (thanks to Robi Rahman for his help)!
A few thoughts on ML/AI safety which may or may not generalize:
You should read successful candidates’ SOPs to get a sense of style, level of detail, and content c.f. 1, 2, 3. Ask current EA PhDs for feedback on your statement. Probably avoid writing a statement focused on an AI safety/EA idea which is not in the ML mainstream e.g. IDA, mesa-optimization, etc. If you have multiple research ideas, considering writing more than one (i.e. tailored) SOP and submit the SOP which is most relevant to faculty at each university.
Look at groups’ pages to get a sense of the qualification distribution for successful applicants, this is a better way to calibrate where to apply than looking at rankings IMO. This is also a good way to calibrate how much experience you’re expected to have pre-PhD. My impression is that in many ML programs it is very difficult to get in directly out of undergraduate if you do not have an exceptional track-record e.g. top publications, or Putnam high scores etc.
For interviews, bringing up concrete ideas on next steps for a professor’s paper is probably very helpful.
My vague impression is that financial security and depression are less relevant than in other fields here, as you can probably find job opportunities partway through if either becomes problematic. Would be interested to hear disagreement.
Great advice! Thanks for sharing :)
A bunch of this definitely does generalize, especially:
“If you have multiple research ideas, considering writing more than one (i.e. tailored) SOP and submit the SOP which is most relevant to faculty at each university.”
“Look at groups’ pages to get a sense of the qualification distribution for successful applicants, this is a better way to calibrate where to apply than looking at rankings IMO. This is also a good way to calibrate how much experience you’re expected to have pre-PhD.”
And if you can pull this off, you’ll make an excellent impression: “For interviews, bringing up concrete ideas on next steps for a professor’s paper is probably very helpful.”
CS majors and any program that’s business relevant (e.g. Operations Research and Financial Engineering) have excellent earning/job prospects if they decide to leave partway through. I think the major hurdle to leaving partway through is psychological?
For people that want to discuss this more and learn from each other’s experiences, or request for coaching, do check https://effectivethesis.org/
Effective Thesis is awesome! I will mention their coaching services in the top post :)