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 an A in an NYU NLP or computational linguistics course or have published research in my area. If that's you, send me an email with a CV, a transcript, and a couple of sentences about your plans and research interests.
Prospective Graduate Students
I'm on leave for 2022–24, potentially continuing into 2025. I may accept new coadvised PhD students during the 2023-2024 admissions cycle, but I am unlikely to be available as a primary solo advisor. If you are interested in working largely or entirely with me, I would advise against applying unless we are already in contact.
I'm especially interested in applicants who have a background with language model alignment, especially where this overlaps with evaluation, data collection, and human-computer interaction. If your background or interests don't look like what I'm describing, we probably won't a good fit this year. In particular, I'm not likely to admit students whose primary interests are in BERTology, cross-lingual transfer, deep learning for linguistic theory, or grammar induction, even though I've worked on those areas in the past.
I can accept 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 have specific questions about my group that you'd like to discuss before you apply, feel free to write to me. Writing to just express interest doesn't help your application, though, and I won't reliably be able to apply. 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 can'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 criteria for all three programs are similar, so you should apply to whichever best fits your record and your interests, though if you're undecided between CS 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.
Under NYU Arts and Science rules, you can't apply to more than one of these programs in the same year.
Prospective Postdocs, Staff, and Visitors
If you'd be interested in working with me as a postdoc or staff researcher, get in touch. I can sometimes make positions available when there's a very good fit, though I can't promise to reply to every inquiry.
I'm able to host visiting students and faculty, but only those who have published work on a research topic that's narrowly of interest to the lab. Get in touch if you're interested, though I can't promise to reply to every inquiry.
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.