Prospective Students

For prospective research assistants and PhD applicants.

It is always a pleasure to have the opportunity to talk and work with motivated researchers interested in human-agent collaboration research at the intersection of HCI and NLP. The most efficient way to express your interest is to fill out the form below. I review applications periodically and will reach out if there is a good fit.

→ Fill Out This Form to Express Your Interest

Frequently Asked Questions

If your question isn’t covered below, you’re welcome to email me directly (my address is on the About page). To help me triage and keep your message from getting buried, please use a structured subject line: lead with a clear bracketed tag identifying the email as a prospective-RA inquiry, follow with your name, and end with a short topic phrase. Generic subjects often get lost in my inbox.

  • Are you currently recruiting?
    Yes. I’m always open to hearing from motivated researchers, and I review applications periodically.

  • What kind of student are you looking for?
    Self-motivated researchers who hold themselves to a high standard. I’m looking for people genuinely excited to explore open questions, not students treating research as another homework assignment or engineers waiting to be handed tasks.

  • Is this a paid, volunteer, or course-credit position?
    The first project is volunteer. Once we’ve established a strong working fit, a paid RA position can be discussed.

  • Can I work remotely, or do you require in-person at Northeastern?
    Remote work is welcome. Most projects run asynchronously and don’t require physical presence.

  • What is the typical time commitment and duration?
    It depends on the responsibility. New RAs typically start by supporting an ongoing project, which gives both of us time to gauge whether our styles fit. After that, you may take on a project of your own based on our mutual assessment. Experienced RAs may lead a project from the start.

  • Should I email you separately after submitting the form?
    No need. The form notifies me automatically; I’ll reply directly if I see a fit.

  • I’m an undergraduate / master’s student / PhD applicant. Does that matter?
    Not really. We’ve consistently seen high-potential undergraduates do excellent work and have very positive experiences with us. What matters is curiosity and self-motivation, not your degree level.

  • What’s the best preparation if I want to apply later?
    Hold yourself to a high standard. Seek mentorship on research skills, and read papers broadly to map the field. Also engage deeply with a few that are high-quality and highly relevant to the direction you want to pursue.

  • Will I get co-authorship if I contribute meaningfully?
    Yes. RAs who make substantive contributions are co-authors on the resulting paper. Authorship and responsibilities are discussed explicitly at the start of each project; the corresponding author makes the final decision on author order and the list of coauthors. Any subsequent changes to the author list require consensus among all coauthors.

  • What does day-to-day collaboration look like?
    Each project has a weekly meeting led by the leading student, plus async communication on Slack and smaller private meetings among coauthors as needed.

  • What tools or programming languages should I be comfortable with?
    Honestly, in 2026, the most important skill is working effectively with AI. Programming and AI assistance are increasingly inseparable. What I look for is the ability to articulate your needs clearly, specify what you want, identify issues in AI output, recover gracefully when things break, and learn from your interactions with AI rather than being driven by it.

    That said, fundamentals still matter, and the right baseline depends on your direction:

    • Prospective PhD students (especially in CS): solid coding ability without AI. Definitely not LeetCode-medium level, but enough that you can write or edit code yourself when that’s faster than describing the change to an AI.
    • HCI students: comfort (or at least familiarity) with qualitative coding methods and survey platforms (e.g., Qualtrics). It’s fine if you’ve never used them. Just do some basic homework to understand what these methods are, and bring willingness to learn.