Join My Research Group at George Mason University

I am recruiting fully funded PhD students for the Fall 2026 cohort (applications are submitted in Fall 2025). I seek motivated and talented students to join my interdisciplinary research group at the intersection of Human-Computer Interaction (HCI), Learning Science, and Artificial Intelligence (AI). We build AI-powered tools and methods that help people create, improve, and learn from educational content at scale. Current potential areas of work include:

  • Learnersourcing & crowdsourcing for content creation (e.g. student-authored questions and explanations).
  • Human–AI partnerships for instructional design, including LLM-assisted feedback and co-creation workflows.
  • Automated evaluation of assessment quality, especially MCQs, combining rubrics, NLP/LLMs, and psychometrics/IRT.
  • Tooling and platforms (e.g. open-source packages and courseware integrations) that make these methods usable by instructors, students, and learning engineers.

Why Mason & Why My Group

By working with me, you can expect to gain practical skills, publish in top-tier venues, connect with a network of outstanding collaborators in academia and industry, and most importantly improve the state of education across the world!

  • R1 research, D.C. access. Mason is an R1 university in the U.S. ranked 20th in HCI and 34th in CS. It's just ~15 miles from Washington, D.C., with campuses across Northern Virginia and a growing innovation hub at Mason Square in Arlington.
  • Industry & government connections. The region’s tech corridor (including Amazon HQ2) and proximity to federal agencies enable internships, partnerships, and applied research with real-world impact.
  • Interdisciplinary home base. In the College of Engineering and Computing (IST), you’ll collaborate across AI/ML, HCI, data, and learning science. Ready to see your work make a real-world impact? We appreciate interdisciplinary talent, empowering you to combine multiple methods and solve problems that go beyond a .001% model improvement.
  • Mentorship & momentum. I keep a hands-on, right-sized group with regular 1:1s, paper planning, and an emphasis on reproducible, deployable research. Especially early on, where I'll really want to make sure my first few students shine bright!
  • Visible outcomes. We target rigorous experiments, useful tools/datasets, and publications at venues such as AIED, Learning@Scale, EDM, CHI, and ACL.
  • Advisor Fit. Finding the right advisor that you fit with is probably the secret sauce to a successful PhD. Definitely reach out to folks who've worked with me, such as former students or colleagues, they'll tell you I tend to color outside the academic lines 💅. I have a healthy work/life balance and hope my students will too!

What I’m Looking For

Strong candidates typically bring a few of the following:

  • Background in CS, HCI, Learning Sciences, or related areas.
  • Programming experience (ideally Python) and comfort with data.
  • Familiarity with machine learning/LLMs and interest in applied NLP.
  • Excellent writing and communication; willingness to iterate and accept feedback.
  • Curiosity about experimental design, user studies, and/or education research.

Nice-to-haves (not required): publications; experience with IRB/user studies; prior work on educational content creation/evaluation; experience deploying web apps.

How to Reach Out (and Stand Out)

Please email me with the subject line [Prospective PhD Student – Fall 2026] and include some brief information about yourself and why you're interested, this could include one or more of the following:

  • CV (with links to publications/projects/code).
  • A few sentences on your research goals and why you want to pursue a PhD with my group.
  • Any of my prior papers you found interesting, to help me gauage your research interest and fit.
  • Representative work that you might have and that you would like to show me (e.g. paper, thesis, or project).

Admissions & Funding

  • PhD admissions are managed centrally by the department; you’ll apply through GMU. I cannot pre-admit you, but I can advocate for strong fits.
  • Funding: admitted PhD students are supported either through a Graduate Research Assistantship (GRA) or Graduate Teaching Assistantship (GTA) positions that includes a competitve stipend and tuition per departmental policies.
  • Timeline: for Fall 2026 entry, plan to submit in the Fall 2025 application cycle.

Fit Check ✨👀🕺: Is This Lab Right for You?

  • You want to design and study human–AI collaboration in education.
  • You enjoy building (datasets, prototypes, analyses) and evaluating with real learners and instructors.
  • You care about research that ships: open-source tools, community benchmarks, and reproducible methods.

For Master’s & Undergraduate Students

I’m happy to involve GMU MS and undergraduate students in research. Please email with your interests, skills, and weekly time commitment. If you’re seeking capstone or thesis supervision, include timelines and any program requirements.

Values & Mentorship

I strive for a lab culture that is supportive and collaborative. We celebrate clear writing, reproducible methods, constructive feedback, and work that has real impact, benefiting users, learners, and educators. My goal is to help you develop an independent research identity and a strong portfolio, whether you pursue academia, industry, or something in between.