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 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 couple of sentences 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) or MLLU (LING-UA 52) as soon as you can and before you reach out to me for one-on-one research supervision. I designed both courses 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.
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.
I may be able to take on one new PhD student for a Fall 2020 start, for work involving crowdsourcing for language understanding tasks, transfer learning for language understanding tasks, or neural network models for applications in linguistics.
Admissions for positions in NLP and computational linguistics are are extremely competitive, and becoming more so. There are no hard rules and I am interested in students with unusual backgrounds. Under almost all circumstances, though, the applicants who I advocate to admit will (i) have already published work in my subfield at highly competitive publication venues and (ii) have at least two detailed recommendations from researchers who frequently publish in those venues.
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 unlikely to hire an additional postdoc soon, but if you have a plan of work in mind that you can only do at NYU, gett in touch just in case there are any avenues available.
I'm able to host visiting students and faculty, but only those who have published work on a research topic that's of interest to the lab.
Consulting RequestsUnless we have collaborated on research already, I'm not generally available for paid or informal short-term industry consulting work.
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.