Advising and Admissions
Current NYU Students
If you're working on a PhD and are interested in collaborating or seeking an advisor, contact me.
I only rarely agree to supervise undergraduate or master's students, and even then I only recruit students who have already taken some coursework in computational linguistics or NLP and done extremely well. If that's you, send me an email six weeks before the start of the term with a CV, a transcript, and a very short note about your plans and research interests.
If your goal is to get started with independent research in NLP, you should take NLU (DS-GA 1012) as soon as you can and before you reach out to me for one-on-one research supervision. I designed that class to walk you through your first major NLP research project, and both the prerequisites and the non-project-related assignments are relatively light.
Prospective Graduate Students
I can advise graduate students in the Department of Linguistics (PhD), the Center for Data Science (MS, PhD), and the Department of Computer Science at Courant (MS, PhD). If you're interested, you're welcome to contact me, though get too many inquiries to be able to read and reply to them reliably. As above, if you're applying for an MS, I won't commit to supervising you as a research student until after you've taken coursework in NLP at NYU. I will likely be able to recruit a new PhD student to start in 2019.
In the interest of fairness (and my sanity), I don't hold interviews or admissions-related meetings with prospective students until after we have received and reviewed everyone's applications. I never hold interviews with MS or undergraduate applicants.
At the PhD level, the Linguistics program offers students a full five-year fellowship, while funding for CS and Data Science students, though guaranteed, often comes through grants for research on specific areas, which flow through advisors. This leads to somewhat different expectations for admission. Linguistics will admit students without a close fit to an advisor, so it's important that the applicant already be quite independent and have a good fit to the department overall. In CS and Data Science, fit to the department is less important, but it's crucial for applicants to name specific potential advisors and to demonstrate (i.e., through reference letters and published/publishable written work) that they're ready to work on problems that those advisors are likely to be interested in (and able to write grants for). Admission rates for all three programs are similar (and low), so you should apply to whichever best fits your record and your interests, though if you're undecided between Computer Science and Data Science, go for Data Science. For students broadly interested in cognitive science, this page offers some useful information about the available programs at NYU.
Prospective Postdocs and Visitors
I'm hiring a postdoc! See here.
I can't offer full funding for internships or other visiting positions, though I may be able to host and help support visitors who have already secured outside funding, at least when there is a clear fit. I can host undergraduate visitors, but only if they have research records and references comparable to those of early-year PhD students at strong US departments. I do not expect to be available to host visitors in Summer 2019.
My paper on this topic with Luke Vilnis was done during a Google internship, and we were not able to take any code or data with us at the end of the internship. If you need help applying VAE language models in new areas, my coauthor Luke has some notes that we are allowed to share, but your best bet is to look at any of the many good papers on the topic that have come out since ours.