was1984

Anyone heard back from MIT's EECS division? Anyone from Area 4 (Physical Electronics) ?

I finally received my first acceptance. This does take off the edge a bit, but right now I'm so giddy I'm being equally unproductive.

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I am currently a 3rd year at UCLA in Bio-Engineering with a cumulative gpa of 3.3 (as of the end of my 1st quarter of my 3rd year). I am interested in going to masters in BioEng. and will apply starting next year (I have 3 - 4 quarters remaining before I apply).
Some of the schools I am considering:
-Univ. of Michigan, Ann Arbor
-UCSD
-Duke
-UCLA
-Washington University in St. Louis
-Rice
-UC Davis

-UC Irvine
-Case Western
Other details:
I have not yet taken my GRE's yet (will do soon).
I have been doing research since the start of my 2nd year and will continue to do so until I graduate.
I have got an authorship on a recently published paper (2nd/3rd author).
I am pretty sure I will have good teacher recs (from my professors and my post doc fellow who I work under at research).
What colleges from the above, if I were to apply right now (assuming that I got a mediocre GRE score) would I get accepted into their Master's program?
To better my chances, how much do I need to increase my GPA to, REALISTICALLY?
Thanks.

Hi, I wonder if anyone would share with me which good graduate school is still accepting application for PhD and funding?

I lived in Austin for about 6 years. The last two years have been a lot colder than usual. Typically it will get below freezing around 5 days a year, overnight. It's fairly common to have daytime highs in the mid 60's even midwinter. Occasionally it will even jump up into the 70s.

Winter is honestly the best time of year in Austin, because summers are pretty brutal. Expect 20 or so days above 100 degrees (sometimes well above 100 degrees) with high humidity. It's usually above 90 between early June and early September and it's rare that the high temperature is below that in that period.

Based on last year Berkeley should be coming out this week. Good luck everyone!

Warning: nerdy and completely useless except as game; if I didn't want to spend a totally unreasonable number of hours engaged in pointless intellectual pursuits, I wouldn't be applying to grad school My math or reasoning in general might be off; if it is; call me on it. Note also that I'm being a Bayesian about things, so read "there is x% of y" as "you should estimate the probability of y at x%."

So, you've applied to some set of schools. For most of them, Peterson's lists their number of applicants, admissions rates, and actual number of attenders from each class. How would one produce an unbiased estimate of your chances of universal rejection (and, possibly, a few other things) from just this information? (Of course if you have more information than this, you would want to make the model more complex to incorporate that extra information, and I'd love to see models that incorporate GPA/GREs, &c.)

a : the number of admissions you will actually receive
n : the number of schools to which you have applied
pi : the admissions rate of the ith school to which you have applied (the order isn't important)

Prior assumption: for the schools for which you have applied, you have no particular reason to believe that you are especially more or less competitive than the typical applicant. This doesn't mean that you expect to be exactly in the middle - if that case you know you would be universally rejected, assuming admissions rates are all below 50% - but that you expect a 1% chance of being in the first percentile of competitive applicants, a 2% chance of being in 2nd percentile or better, a 3% chance of being in the third percentile or better, and so on. If you can accept this prior, your chances of being accepted into school i is, conveniently enough, px, and the average expected number of schools you will get into is

μa = Σni=1pi = p1 + p2 +p3 + ... + pn

or the additive sum of their admission rates. However, you don't know how well these are correlated with each other. If they're maximally correlated - they all admit students on precisely the same criteria - then your chances of a wipeout are equal to the complement of the most favorable admissions rate among your schools; if they are totally uncorrelated, your chances of a wipeout are equal to the multiplicative sum of their complements; if if maximally negatively correlated, then your chances of a wipeout are min(0,1 - μa). Common sense says that they should be positively but not maximally correlated, but how much? Fortunately you know

b : the number of admittances schools in your field send out divided by their number of graduate students per year, where "field" is selected such that its competitiveness roughly reflects the competitiveness of the set of schools to which you have applied

(Sneaky assumption: the number of those in your field you are admitted to grad school and choose not to go at all is zero, or at least small enough to be ignored.)

Thus we know that a randomly chosen applicant in your field - someone, by the first prior, who is as competitive as you - should expect, given that she is accepted into any schools, to get into b schools on average. If, as would be convenient, her expected total number of admittances including the possibility of wipeout is the same as yours, μa, then your/her chances of a wipeout, p(a=0) are

μa = 0*p(a=0) + b*(1-p(a=0))
μa/b = 1 - p(a=0)
p(a=0) = 1 - μa/b

If you haven't applied to the typical number of schools

However, this randomly chosen applicant, who is as competitive as you, isn't necessarily applying to as many schools as you - she's applying to n̄ of them, which might be more or less - although by assumption we suppose the schools she applies to are as competitive as your own. So in fact her expected number of admissions, μā = μan̄/n and

μan̄/n = 0*p(ā=0) + b*[1-p(ā=0)] = b*[1-p(ā=0)]
μan̄/bn = 1 - p(ā=0)
p(ā=0) = 1 - μan̄/bn
n̄ = bn * [1 - p(ā=0)] / p(ā=0)

If we knew n̄, we could know p(ā=0) as well - or visa versa - and thus

p(a=0) = p(ā=0)^(n/n̄)
p(a=0) = (1 - μan̄/bn)^(n/n̄) or p(a=0) = p(ā=0) ^ { p(ā=0) / b[1 - p(ā=0)] }

Can we produce n̄ or p(ā=0) independently? Unfortunately I don't see a way to do so, limiting yourself to the Peterson's data. Choose a number that seems reasonable for one or the other based on anecdotal evidence, or find some publicly available data (and post it here, ideally.) But either way an estimate of one should get you to p(a=0). This should also give you B=μa|a>0, the expected number of schools you get into in the event that you get into any schools at all:

μa = 0*p(a=0) + B*[1-p(a=0)]
B = μa / [1-p(a=0)]
p(a=0) = 1 - μa/B

Revising in light of results

All of the above assumes that you haven't heard back from any schools yet. If you get an acceptance or rejection, how should that affect your expectations of getting into other schools? Unfortunately the ratio of acceptances to grad students doesn't tell us what the distribution of acceptances among the admitted is.

Suppose you hear back from your first institution, University Q - an acceptance. Will you get into another? According to Bayes' theorem,

p(a>1)|into Q = pQ|a>1 * p(a>1) / pQ

Only pQ is a known constant, so we need to guess pQ|a>1 * p(a>1).

pQ|a>1 is the chance that, given that you got into more than one school, one of those schools was Q. This is equal to

pQ|a>1 = (pQ - pQ|a=1) / p(a>1)

so

p(a>1)|into Q = [ (pQ - pQ|a=1) / p(a>1) ] * p(a>1) / pQ
p(a>1)|into Q = (pQ - pQ|a=1) / pQ

(One intuitive, but clearly wrong, estimate of the chance of admittance to Q given only one admission is

pQ|a=1 = (pQ / μa) leading to
p(a>1)|into Q = [pQ - (pQ / μa)] / pQ
p(a>1)|into Q = 1 - (1 / μa)

This implies that an acceptance from one school is nearly as good a signal as a decision from another, and in fact that getting into an easier school should revise your expectations up more than getting into a harder school - prior to learning anything, you have a higher expectation of getting into at least one school other than your reach than getting into at least one school other than your safety, but in fact getting into your reach and into your safety brings their chances to the same level. In fact if there's any overlap between expected admissions at all then the chance of being admitted to an easier program but not a harder program is not only more likely than the reverse, but in a way that exaggerates their independent probabilities.)

One obvious method is to use recursion: imagine someone, as competitive as yourself, who applied to every program but the one you've just heard back from, i.e. μa2 = μa - pQ, n2=n-1,n̄ remains constant, and her field is your field, such that

B2 = (μa - pQ) / [(1- { p(ā=0) ^ [ (n-1) / n̄ ] }]
p(a2=0) = 1 - [ (μa - pQ)/B2 ]

In that case, p(a=0) - p(a2=0) = pQ|a=1, and - if we want to write out a big ridiculous equation -



p(a>1)|Q = {1+ pQ + {(1- [ p(ā=0)(n-1)/n̄ ](μa + pQ)}/(μa - pQ) - p(ā=0)n/n̄ } / pQ


p(I made some sort of obvious arithmetic mistake or worse) > 0.5, so the above is most likely nonsense. If it's right then calculating how to update your chances in case of a rejection should be trivial.

Got an interview invite email from JHU BME yesterday, the interview dates are Feb 24-25 or March 11-12. Scheduling is getting interesting since the dates conflict with other potential schools...

I finally received my first acceptance. This does take off the edge a bit, but right now I'm so giddy I'm being equally unproductive.

Seems like MIT's EECS admission committee is finalizing today and tommorow :

http://eecscal.mit.edu/Pages/CalendarViews/MonthView.aspx?RoomId=1&Date=2%2f3%2f2011+12%3a00%3a00+AM

Those of you who are offended by curt replies need to develop some thicker skin of you are going to be successful graduate students. Most faculty members are -extremely- busy people and they are dealing with a lot of these types of emails right now. If they sat down and wrote a well thought out reply to everyone that has emailed them wanting to work for them, that would probably be an entire day or more wasted on that process.

Plus, people who are emailing professors right now are indeed being a tad bit annoying. It's totally fine to email a professor to verify that they are a good research fit before applying to the school, but at this point you've already applied and if you are an appropriate fit your application will be reviewed. Emailing potential advisers right now is superfluous and irritating.

Those of you who are offended by curt replies need to develop some thicker skin of you are going to be successful graduate students. Most faculty members are -extremely- busy people and they are dealing with a lot of these types of emails right now. If they sat down and wrote a well thought out reply to everyone that has emailed them wanting to work for them, that would probably be an entire day or more wasted on that process.

Plus, people who are emailing professors right now are indeed being a tad bit annoying. It's totally fine to email a professor to verify that they are a good research fit before applying to the school, but at this point you've already applied and if you are an appropriate fit your application will be reviewed. Emailing potential advisers right now is superfluous and irritating.

Those of you who are offended by curt replies need to develop some thicker skin of you are going to be successful graduate students. Most faculty members are -extremely- busy people and they are dealing with a lot of these types of emails right now. If they sat down and wrote a well thought out reply to everyone that has emailed them wanting to work for them, that would probably be an entire day or more wasted on that process.

Plus, people who are emailing professors right now are indeed being a tad bit annoying. It's totally fine to email a professor to verify that they are a good research fit before applying to the school, but at this point you've already applied and if you are an appropriate fit your application will be reviewed. Emailing potential advisers right now is superfluous and irritating.

This is why I could never be a social scientist. Arbitrarily assigning values to things gives me the heebie-jeebies.

One of my schools sent an email today with a link to a survey about what they can do to improve their web presence. My. heart. almost. stopped...until I saw the subject line.

That's just cruel.

Those of you who are offended by curt replies need to develop some thicker skin of you are going to be successful graduate students. Most faculty members are -extremely- busy people and they are dealing with a lot of these types of emails right now. If they sat down and wrote a well thought out reply to everyone that has emailed them wanting to work for them, that would probably be an entire day or more wasted on that process.

Plus, people who are emailing professors right now are indeed being a tad bit annoying. It's totally fine to email a professor to verify that they are a good research fit before applying to the school, but at this point you've already applied and if you are an appropriate fit your application will be reviewed. Emailing potential advisers right now is superfluous and irritating.

Those of you who are offended by curt replies need to develop some thicker skin of you are going to be successful graduate students. Most faculty members are -extremely- busy people and they are dealing with a lot of these types of emails right now. If they sat down and wrote a well thought out reply to everyone that has emailed them wanting to work for them, that would probably be an entire day or more wasted on that process.

Plus, people who are emailing professors right now are indeed being a tad bit annoying. It's totally fine to email a professor to verify that they are a good research fit before applying to the school, but at this point you've already applied and if you are an appropriate fit your application will be reviewed. Emailing potential advisers right now is superfluous and irritating.

Those of you who are offended by curt replies need to develop some thicker skin of you are going to be successful graduate students. Most faculty members are -extremely- busy people and they are dealing with a lot of these types of emails right now. If they sat down and wrote a well thought out reply to everyone that has emailed them wanting to work for them, that would probably be an entire day or more wasted on that process.

Plus, people who are emailing professors right now are indeed being a tad bit annoying. It's totally fine to email a professor to verify that they are a good research fit before applying to the school, but at this point you've already applied and if you are an appropriate fit your application will be reviewed. Emailing potential advisers right now is superfluous and irritating.

Providence is a small wintery town. During the winter you're like burried in snow. It can be quite depressing. In addition, I don't really see the point in attending Brown University. It is a second rate University at best!

This is why I could never be a social scientist. Arbitrarily assigning values to things gives me the heebie-jeebies.

This is why I could never be a social scientist. Arbitrarily assigning values to things gives me the heebie-jeebies.

I'd suggest you take a class on ethics. Does going back on your word mean that little to you?

thank you so much for prompt attention....
but i'm seeking a global help....a little more than you think