Grad school survey, revisited

You may recall a while ago I ran a survey on where people applied to grad school. Obviously I've been sitting on these results for a while now, but I figured since it's that time of year when people are choosing grad schools, that I would say how things turned out.  Here's a summary of things that people thought were most important (deciding factor), and moderately important (contributing factor, in parens):

• Academic Program
• Specialty degree programs in my research area, 48%
• (Availability of interesting courses, 16%)
• (Time to completion, 4%)
• Application Process
• Nothing 
• Faculty Member(s)
• Read research papers by faculty member, 44%
• Geographic Area
• (Outside interests/personal preference, 15%)
• Recommendations from People 
• Professors in technical area, 45%
• (Teachers/academic advisors, 32%)
• (Technical colleagues, 20%)
• Reputation
• ... of research group, 61%
• ... of department/college, 50%
• (Ranking of university, 35%)
• (Reputation of university, 34%)
• Research Group
• Research group works on interesting problems, 55%
• Many faculty in a specialty area (eg., ML), 44%
• (Many faculty/students in general area (eg., AI), 33%)
• (Research group publishes a lot, 26%)
• Web Presence
• (Learned about group via web search, 37%)
• (Learned about dept/univ via web search, 24%)
• General
• Funding availability, 49%
• (High likelihood of being accepted, 12%)
• (Size of dept/university, 5%)

Overall these seem pretty reasonable.  And of course they all point to the fact that everyone should come to Maryland :P.  Except for the fact that we don't have specialty degree programs, but that's the one thing on the list that I actually think is a bit silly: it might make sense for MS, but I don't really think it should be an important consideration for Ph.D.s.  You can get the full results if you want to read them and the comments: they're pretty interesting, IMO.

ACL / ICML Symposium?

ACL 2011 ends on June 24, in Portland (that's a Friday). ICML 2011 begins on June 28, near Seattle (the following Tuesday). This is pretty much as close to a co-location as we're probably going to get in a long time. A few folks have been discussing the possibility of having a joint NLP/ML symposium in between. The current thought is to have it on June 27 at the ICML venue (for various logistical reasons).  There are buses and trains that run between the two cities, and we might even be able to charter some buses.

One worry is that it might only attract ICML folks due to the weekend between the end of ACL and the beginning of said symposium. As a NLPer/MLer, I believe in data. So please provide data by filling out the form below and, if you wish, adding comments.

If you woudn't attend any, you don't need to fill out the poll :).

The last option is there if you want to tell me "I'm going to go to ACL, and I'd really like to go to the symposium, but the change in venue and the intervening weekend is too problematic to make it possible."

Which would you attend?
	Only ACL
	Only ICML
	ACL and the symposium
	ICML and the symposium
	Only ACL and ICML
	All three
	Only ACL, because of convenience
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Getting Started In: Bayesian NLP

This isn't so much a post in the "GSI" series, but just two links that recently came out. Kevin Knight and Philip Resnik both just came out with tutorials for Bayesian NLP. They're both excellent, and almost entirely non-redundant. I highly recommend reading both. And I thank Kevin and Philip from the bottom of my heart, since I'd been toying with the idea of writing such a thing (for a few years!) and they've saved me the effort. I'd probably start with Kevin's and then move on to Philip's (which is more technically meaty), but either order is really fine.

• Bayesian Inference with Tears by Kevin
• Gibbs Sampling for the Uninitiated by Philip

Thanks again to both of them. (And if you haven't read Kevin's previous workbook on SMT -- which promises free beer! -- I highly recommend that, too.)

Where did you Apply to Grad School?

Ellen and I are interested (for obvious reasons) in how people choose what schools to apply to for grad school. Note that this is not the question of how you chose where to go. This is about what made the list of where you actually applied. We'd really appreciate if you'd fill out our 10-15 minute survey and pass it along to your friends (and enemies). If you're willing, please go here.

Programming Language of Choice

Some of you know that I (at least used to be) a bit of a programming language snob. In fact, on several occasions, I've met (in NLP or ML land) someone who recognizes my name from PL land and is surprised that I'm not actually a PL person. My favorite story is after teaching machine learning for the second time, I had Ken Shan, a friend from my PL days, visit. I announced his visit and got an email from a student who had taken ML from me saying:

I _knew_ your name was familiar! I learned a ton about Haskell from your tutorial, for what's worth.. Great read back in my freshman year in college. (Belatedly) Thanks for writing it!

And it's not like my name is particularly common!

At any rate (and, admittedly, this is a somewhat an HBC-related question) I'm curious what programming language(s) other NLP folks tend to use. I've tried to include a subset of the programming language shootout list here that I think are most likely to be used, but if you need to write-in, feel free to do so in a comment. You can select as many as you would like, but please just try to vote for those that you actually use regularly, and that you actually use for large projects. Eg., I use Perl a lot, but only for o(100) line programs... so I wouldn't select Perl.

What programming language(s) do you use for large-ish projects?
	C/C#/C++/Objective-C
	D
	Eiffel
	Erlang
	F#
	Haskell
	Java
	Lisp
	Matlab
	OCaml/SML/ML
	Perl
	Python
	R
	Ruby
	Scala
	Sheme
	Smalltalk
	Other
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Survey of Parser Usage

I know people use parsers a fair amount; I'm curious which parsers people use. Thus, a new poll :). What I'm interested in, in particular, is if you use a parser essentially as a preprocessing step before doing something "higher level." In other words, if you are Mark Johnson and you use the Charniak parser to build a better parser, this doesn't count (sorry, Mark!). I want to know if you use one to do, eg., summarization or MT or IE or something other than parser... (Also, I don't count shallow parsing in this context.) I look forward to responses!

Do you use a parser for your work?
	Nope, no parsing in this neck of the woods!
	Yes, one of the Brown parsers (Charniak, Johnson, etc.)
	Yes, one of the Collins' parsers (Collins, Bikel, etc.)
	Yes, MiniPar
	Yes, one of the CoNLL dependency parsers (Nivre, McDonald, etc.)
	Yes, a rule-based parser.
	Yes, something else entirely.