How Much Do You Weigh Ranking?

[bandinterwebs]

I'm weighing about five different schools right now, and none of them strike me as the "perfect" fit just yet. I still have a few more visits to go, which will help clarify things (I hope).

I *think* it will come down to two schools:
School A is ranked more highly, and I'm up for a fellowship. It is a top 5 program. Unfortunately, I think the department will be more theoretical than I would like and I don't believe it will be a great fit.

School B made me a mediocre funding offer, but I think the profs and research will be a better fit (more applied). For personal reasons, I prefer the location. However, while it is a top 10 program, the school is ranked four slots below School A.

How seriously should I consider ranking? And on top of that, a prestigious fellowship at a higher-ranked university? How much will the ranking of the school I attend for my PhD affect my job prospects? Is it worth turning down a school I feel might be a slightly better personal fit?

I realize this a really large and philosophical (and subjective) question, but I would appreciate any thoughts.

[eternallyephemeral]

In my personal opinion, the rankings at these schools are similar and that means that ranking is not as big of a difference in this case.

The fellowship would be good for multiple reasons, one for the prestige and also for the funding aspect. However, if the mediocre funding offer is offset by you wanting to live there, and it's enough to cover your costs, I could see these aspects balancing out.

The job prospects answer depends on what kind of job you are thinking of. You mentioned liking more applied research, but I don't want to assume that means you want to go so applied as to only be considering industry and not academia. If you're thinking about industry, I don't think that difference in school ranking will mean the difference between getting and not getting a job.

Those things being said, I believe that your productivity is highest in a place you would like to live, where you don't have to constantly worry about the cost of living there, and where you have a great research fit with a supportive and understanding supervisor. This means different things to different people, but these differences can be the difference between excelling in a good program and facing huge difficulties in a better program.

Ultimately, you are the one getting the degree and you should be creating your unique PhD path. I believe you will be good at a good school and at an excellent school, and that the opportunities available are the dealbreakers. If you have the opportunity to do what you want to do, if you have the opportunity to make enough money to live on, and if you have the opportunity to go into the type of career you want (as approximated by where the alumni go), then you made a good choice.

I'm sure some people will disagree, but it depends on your intentions imo.

Best of luck with your decision!

[Dawnbreaker]

Hi @bandinterwebs.. In these cases, I think it would help a lot if you can identify the universities by name, instead of hiding them. That way, you'll get much better and directed responses.

For example, my suggestions will be very different depending on what A is. If A is one of Berkeley or Stanford, I'd highly recommend that you go there. Though the programs are certainly theoretical in nature, the location, possibilities of summer internships, and overall culture will provide the necessary applied "flavor" you might need. On the other hand, I personally think that GATech and Northwestern are not as good as rankings suggest - they end up doing very theoretical work based on last century knowledge. I'd definitely pick a lower ranked program like UIUC or Cornell over them.

PS: Note that I have a bias in my research interests towards statistical decision theory, stochastic control, and convex optimization. All of these are at the interface of IEOR, EECS, and Statistics.

[NeuroBatman]

If you haven't yet, I recommend that you check this out: http://chronicle.com/article/NRC-Rankings-Overview-/124708/

After looking at the numbers on this site, I was able to make a more informed decision.  When you look at the US News rankings, it gives you a single ranking.  Dartmouth's PBS program, which I'm starting in the fall, was ranked in the middle of the pack.  However, after looking at the numbers here, I was able to see that after controlling for 'diversity,' Dartmouth is a top tier program.  Of course, diversity is important, but for me was not a game changer.  In fact, the program is tied for 1st place for student outcomes.  So I wouldn't take a single number as an indicator of quality.

I think having a few metrics to look at will give you a better idea.  Of course, the most important factor is fit with your potential adviser.  I think if you are happy where you are, you will be more productive, which will result in a more impressive CV. 

I don't buy into the notion that you will only be able to get a great job if you go to a top ranked university.  I think what's more important is having a cohesive body of research that contributes to your field.  Regardless of where that research was conducted.

I hope this helped and best of luck.  Cheers!

[bandinterwebs]

Thank-you all for your thoughtful responses. You've given me a lot to mull over.

@Dawnbreaker - As you correctly surmised, School A is Berkeley School B is Virginia Tech. There are a few other schools in the mix, but I think those are the two I'll decide between. You bring up a great point - possibility of local internships will be much greater at Berkeley. Are internships (as compared to staying on to do research) preferable?

@justinhayes1982 - Thank-you for that! Trying to look through those metrics now!

 

[Dawnbreaker]

@bandinterwebs  To be honest, Berkeley and Virginia Tech IMHO is a no contest. Berkeley is far superior in terms of reputation - both in academia and industry. It's also a great time for optimization and data science in general, and specifically in the Bay Area. The only reason to not choose Berkeley is if your research is very focused on particular areas like Supply Chains or Production/Manufacturing. However, as said before analytics, data science, and related problems are all the rage these days. I'm personally very familiar with the work of many OR faculty at Berkeley. Specifically, Javad Lavaei is a superstar in applying convex relaxation methods for distributed problems. Atamturk, Hochbaum, and El Ghaoui are also world leaders in various aspects of optimization and statistical decision theory. Ken Goldberg is of course a very famous Roboticist  - with very close ties (multiple PhD students) to EECS and MechE programs. He uses learning and optimization for motion planning, robotic surgery etc - very interesting work.

It's also worth noting that OR is very close to both the Statistics and EECS departments. Most end up getting an additional MS (with thesis) in either one of them depending on their inclinations. (OR+Statistics) and (OR+CS) are mouthwatering combinations for many job searches. So if you want to go the Industry route, Berkeley will definitely offer more - due to both program structure and location. I think summer internships are encouraged, and most do at least 2-3 summers over their 4-5 year stay in the program. It's also really strong for academia - they placed a recent grad at MIT.

However, the only concern might be the high cost of living and financial situation of IEOR dept and UC Berkeley in general. I believe some 3-4 years ago, IEOR had a funding crunch. I think they have recovered well, but you should check with students and professors. How much are they paying you, and have they guaranteed full funding?

PS: I realize that this sounds like a sales pitch, and honestly Berkeley doesn't need one. However, I am joining there and plan to work closely with many OR professors. Most of the above info are based on my own research and interaction with people there. As I said before, Berkeley and VTech is a no-contest. Berkeley and Stanford are the best places for new-age OR (intersection of optimization, statistics, and computing).

[bandinterwebs]

16 minutes ago, Dawnbreaker said:

@bandinterwebs  To be honest, Berkeley and Virginia Tech IMHO is a no contest. Berkeley is far superior in terms of reputation - both in academia and industry. It's also a great time for optimization and data science in general, and specifically in the Bay Area. The only reason to not choose Berkeley is if your research is very focused on particular areas like Supply Chains or Production/Manufacturing. However, as said before analytics, data science, and related problems are all the rage these days. I'm personally very familiar with the work of many OR faculty at Berkeley. Specifically, Javad Lavaei is a superstar in applying convex relaxation methods for distributed problems. Atamturk, Hochbaum, and El Ghaoui are also world leaders in various aspects of optimization and statistical decision theory. Ken Goldberg is of course a very famous Roboticist  - with very close ties (multiple PhD students) to EECS and MechE programs. He uses learning and optimization for motion planning, robotic surgery etc - very interesting work.

@Dawnbreaker - there's the rub. I'm into Production and Logistics systems, specifically in industrial settings. I am absolutely agreed with you on everything else. It's a better school overall, and if the fellowship comes through, I won't be too concerned about the cost of living. It would also be nice to have access to such a strong computer science program, since I'd like to explore a dual masters, if possible. But fit is part of the question with Berkeley. VT seems to have greater faculty focused on applied production and logistics research. 

By the way, you're going there for math or stats or IE/OR? If IE/OR, will you be at the visit?

 

[Dawnbreaker]

Hi @bandinterwebs.. As my signature suggests, I'll be joining MechE. However, my research interests are much more on the optimization, control, and statistics end of things. I'm actually trying to see if I can get one of Profs. Goldberg or Lavaei as my adviser or co-adviser. I got admitted for Fall 2015, and deferred admission. So had a ton of time to do groundwork on various programs.

I am not 100% sure about production and logistics. How fixed are your interests? I mean, Berkeley did admit you, so there must be some research interest overlap! Check out Rhonda Righter and Lee Flemming. They seem to be doing related work. Btw, have they already approved your fellowship, or are you on some sort of wait-list? I assume you'll get TA/RA if you don't get the fellowship. Cost of living is certainly a concern, but definitely manageable with a little compromise and wise spending.

[Dawnbreaker]

@bandinterwebs Also look at people from Haas, some people there also seem to do relevant work.. Anyway, to answer the opening question, I personally think the ranking difference in this case is quite significant. I'd highly recommend Berkeley. Best wishes for your decisions and grad school experience  

[bandinterwebs]

Thank-you @Dawnbreaker! I think there are a precious few profs who do relevant (to me) work, such as Yano (who is also involved in Haas), so I'm eager to see how it is when I go out for my meetings/visit.

I should hear back on the fellowship within the next few days, which would certainly be an influencing factor.

Appreciate all the advice!

[juilletmercredi]

Any ranking differences within schools in the top 10 are negligible, especially in a field like engineering (where there are more jobs than people). Of more concern is the funding. I'd never attend a PhD program that didn't offer full funding, so there's that.

You want a good fit, and you don't want to go to a school with a bad fit. If the funding was equal, I'd tell you to go to School B. Even if the funding at School B was less but still adequate to cover your living costs, I would say School B. But if School B's stipend is not large enough to cover basic cost of living, then I'd say go to School A.

After looking at the numbers on this site, I was able to make a more informed decision.  When you look at the US News rankings, it gives you a single ranking.  Dartmouth's PBS program, which I'm starting in the fall, was ranked in the middle of the pack.  However, after looking at the numbers here, I was able to see that after controlling for 'diversity,' Dartmouth is a top tier program.  Of course, diversity is important, but for me was not a game changer.  In fact, the program is tied for 1st place for student outcomes.  So I wouldn't take a single number as an indicator of quality.

I don't think that's how the NRC rankings work. The S-rankings are built on criteria that faculty say are important, and the R-rankings are based on the similarity of a given program to other programs faculty have ranked highly. Just from the information provided on the NRC page, though, you can't 'control' for diversity. You'd have to have access to the original regression analysis that NRC used and add a covariate (or remove diversity) from the methodology. (It's also not really "tied for first place", because as you noted, the NRC gives a range estimate instead of a point estimate. The true ranking of Dartmouth's student outcomes is not actually 1; it is somewhere between 1 and 33.)

I don't buy into the notion that you will only be able to get a great job if you go to a top ranked university.  I think what's more important is having a cohesive body of research that contributes to your field.  Regardless of where that research was conducted.

Unfortunately, both anecdotal evidence from search committees and actual research done on this topic provides evidence for the opposite - ranking/prestige of your doctoral program has a huge impact on where you end up as faculty, such that it's quite possible for a less-published person from a high-ranking program to get a job over a well-published student from a lower-ranked program. See this Science Advances paper: http://advances.sciencemag.org/content/1/1/e1400005

However, this is discussing rather large differences in rankings. Two programs both in the top 10 won't be substantially different on job placement especially in a field like EE.

[Dawnbreaker]

1 hour ago, juilletmercredi said:

Any ranking differences within schools in the top 10 are negligible, especially in a field like engineering (where there are more jobs than people). Of more concern is the funding. I'd never attend a PhD program that didn't offer full funding, so there's that.

I don't think this is accurate, and it doesn't even agree with your last line about Aaron Clauset's findings! 

Let me clarify, I don't believe in rankings as a strict indicator (rank 6 not necessarily better than rank 7). However, there are definite tiers. Implicitly, you are creating a tier yourself with something like "top 10". Why did you come up with this magical number 10, and not something else like 15 or 20? IMHO, it's pretty clear that top 10 is not homogeneous by any means.

For example, in EE; MIT, Stan, UCB, Caltech, and (possibly) UIUC clearly break from the rest. Head to head, MIT will definitely win much more students than Michigan for example. The causation is not high rankings, but rather the understanding that MIT has a higher standard of research, which in turn reinforce the rankings. To say that difference in rankings is negligible is too much of a stretch. In CS, the difference between the top 4 or 5 (MIT, Stan, UCB, CMU, UW) and the rest of top 10 is even more stark. No one with an offer from one of these programs is going to choose something else, unless there are other factors at play like two-body. They also produce much more faculty.

I however agree with you on funding. I wouldn't do a PhD without guaranteed funding. But, I am pretty sure a top school like Berkeley wouldn't admit anyone without funding. I think the OP is just waiting for confirmation on fellowship, which if he doesn't get, should get a TA/RA offer (the same happened with me). With regards to NRC rankings, they are outdated.