I am a Ph.D. candidate in the Language Technologies Institute of the School of Computer Science at Carnegie Mellon University.
I research statistical Natural Language Processing (NLP) and its interaction with social science with my advisors,
Noah Smith and William Cohen.
Understanding a large volume of information is difficult for humans, which poses a unique
challenge on the face of the recent flood of information in the political domain.
There seems to be so much information, yet we seem not to know where to begin to read.
I believe that statistical NLP is uniquely equipped to make a social impact in this context,
since its fundamental pursuit is to understand linguistic phenomenon by
taking advantage of evidence in large numbers.
We hope our research will bear both practical and scholastic importance in this context.
I am collecting lectures/tutorials/technical notes on EM (Expectation-Maximization Algorithm). It feels like whenever I take a class in Machine Learning or NLP, I get a new description of this algorithm. I think its funny. This is what I found so far. Drop me a word if you know interesting ones. Historical documents are welcome. Non-cs documents are welcome :-) (I also collect cool vi tricks.)
First change made 07/14/2009