Graduate School

It’s graduate admissions season, apparently. So far I’ve heard back from six institutions: four acceptances (including a number of my top choices) with fellowships at two of them, one we’ll-fly-you-out ‘interview’, and one phone interview that went rather well… so at the end of the day, figuring out where I want to end up may be nontrivial.

As I noted in a previous comment, my algorithm for deciding where to apply to was pretty simple: I spent a good deal of time soul-searching and deciding (roughly) what I want to do for the next five-ish years, made a list of all the people I thought would make good research advisors to that end, and applied to the n departments that contained the maximal number of the people on my list (where n was determined by how many applications I was willing to fill out; turned out to be thirteen in total - I’m not superstitious).

My algorithm for deciding where to go for grad school will probably be a variant of the algorithm I used to pick my undergrad institution, and again is not too complicated (Sean Carroll has a very nice post on this subject, btw, as does okham as I just discovered):

1. Make a list of all the factors that I care about: for example, number/quality of advisors who I’m interested in (based on various factors like personal interactions, reputation, publication record, how they place people…), how excited I am by current research efforts, intellectual environment, potential for interesting collaborations, other students, location, quality of the department/school/life, bureaucratic requirements, funding, etc.

2. Weight individual factors accordingly: pretty self-explanatory, although this requires a lot of thought.

3. Visit all the places I’m seriously considering/find out as much about them as possible: this is the data collection stage, so that I have a good idea of how various places shape up in terms of the factors I listed. I’m pretty much traveling every weekend from next Friday to the end of March, with a few days in between for the APS March meeting. That’ll be fun.

4. Assign data values corresponding to each factor for each department: i.e. the results of step #3. These data values will obviously have error bars to reflect the subjectivity inherent to the data collection process, but the inverse relationship between error bar size and time spent on step #3 should enable a single-valued result.

5. Plug and chug: go wherever above algorithm says to go. Hey, it worked pretty well for my undergrad.

Speaking of which, I feel compelled to plug Penn. If anyone reading this happens to be a senior who got into Penn (terminology: I refer to UPenn, not Penn State) for something physics / materials science / nanoscience-related, I strongly urge you to think about coming here for grad school. There’s a lot of very exciting work going on here, and a lot of great people to work with - fantastic intellectual environment (I would say Penn does pretty highly on all the factors I mentioned above).

At this point I’m not really giving much thought to grad admissions anymore, although I know people who obsessively check their email/mail/online forums, like thegradcafe. I was looking through the posts there, and came across a few rather unconventional programs, if they’re real:

- Penn State has a Master’s program in “Recreation, Parks and Tourism Management”

- Fordham has a “School Psychology” program

- Someone posted about an “Applicable Math” program at UNC Chapel Hill, although I couldn’t find it, although apparently there’s one at Syracuse (did they decide not to go as far as to actually apply the math?)

- U Minnesota has a “Comparative Studies in Discourse and Society” program

- Quite a few places (I think I saw Syracuse and UC Berkeley) have programs in rhetoric.

…and so on.

~ by Sujit on February 6, 2008.