Hi guys. I’m currently getting a research mscs in a top10 university in US (top10 cs undergrad in the states as well, both rankings are major rankings), and about to apply for CS PhD this fall. Here’s some info:
- GRE: 161 verbal, 170 quant, 4.0 writing
- intern: 3 summer interns in the bay area
- research & publication: 1 first author at ISIT, 1 second author at ACL, 1 second author at EMNLP, 1 third author at EMNLP.
- GPA: undergrad 3.84, grad~3.9
- recommendation letters: my advisors are pretty good, although they may not write super strong recommendations
I would like to do research in learning theory, also tcs or mixed integer programming. Here’s my list:
- reach: UCB, Stanford, MIT, CMU, Princeton, TTIC
- match: UT-Austin, UWashington, UPenn, GeorgiaTech (ACO program), UWaterloo (ACO), UCSD? Cornell?
My concern is that, although I have 4 papers, only 1 is about learning theory, the other 3 are about NLP, which I’m not sure whether would help with applications for learning theory direction. ML is getting very very popular nowadays and I know ML applicants who have a lot of papers get rejected by all the universities they apply… On the other hand, is learning theory a little easier to get in compared to ML? Since the former is theoretical. Also does anyone know how hard it gets to change research topics? I have no research experiences except for some courses (and reading on my own) in TCS or MIP, but they really appeal to me.
So am I being too optimistic about these choices? Thanks a lot for any suggestions!