When studying your folder, we are primarily interested in your potential to do independent research:
- Past research. This is the best evidence of research potential, so please let us know in detail about any original research that you may have done already. Emphasize what was new and important about the problem and what creative or unusual steps you took to solve it. You may wish to include copies of published or unpublished papers.
- Intellectual qualities. We also look for other evidence that you are skilled, creative, and persistent at solving problems. We take letters of recommendation very seriously — and if we are considering you seriously, we will probably contact your recommenders to discuss your intellectual qualities. Thus, the most useful recommendations come from professors or researchers who have discussed ideas with you and know how your mind works.Of course, we also consider your grades, since most strong researchers are also able to do well in classes. (However, doing well in classes does not prove that you will have the creativity and initiative to find new problems and new solutions.) If your grades are mixed, please tell us why.We don’t like to rely too much on the GRE, because it is just an artificial one-day exam. Very high GRE scores are most useful if your recommendations and grades come from a lower-ranked institution: your high GRE will reassure us that you will shine as brightly here as you did there. Surprisingly low GRE scores on an otherwise strong application may just be a fluke, so they do not disqualify you, but they will make us check your application for other signs of weakness. Most of our applicants do not take the GRE subject test unless they want to establish that they know CS despite having a non-CS major.
- Relevant academic background. We sometimes do take exceptional students whose interest in NLP exceeds their background in it. However, we are very interested to learn about your past coursework, class projects, or original research in natural language processing, machine learning (including data mining, probability, or statistics), linguistics, or search/optimization. A good background in any of these areas will help you start doing research here immediately, and will give you a useful perspective as you take classes in the other areas.
- Technical skills. While recognizing that different people have different strengths, I look for evidence of certain skills that are relevant to research in my lab:
- Programming ability, part 1 — strength at building complicated systems and otherwise making software work well.
- Programming ability, part 2 –strength at designing new algorithms or data structures.
- Mathematical ability — strength at formalizing ideas, proving theorems, and reading mathematically dense papers. This may be indicated by strong grades (or advanced coursework) in pure or applied math or theoretical CS.
- Linguistic ability and interest — a sensitivity to the nuances of sentences or words (their internal structure, meaning, and sound or written expression). This may be indicated by coursework in linguistics, a serious interest in writing, knowledge of multiple languages, etc.
- Writing, speaking, and teaching ability — Basic skills that you will need to succeed as a researcher.
- Quality of technical discussion. If I’m your advisor, we’ll be having lots of intense technical discussion over several years. Many of your research ideas, as well as mine, will be born in such discussions. Furthermore, I’ll probably ask you to write them up afterwards in an email.You will also spend a lot of time throwing ideas around with the other grad students. So it is important that you are articulate (in English), energetic, and interesting to talk to.Therefore, before recommending you to the admissions committee, the CLSP faculty will want to spend several hours talking with you, either in person or (for foreign students) over the phone. We want to see that you will pick up new ideas, draw connections to things you already know, ask good questions, and reply with ideas of your own.
- These notes are from Prof. Jason Eisner, Computer Science Dept. and Center for Language and Speech Processing, Johns Hopkins University
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