Mathematics
Linear algebra, calculus, analysis, and numerical optimization
Join us
Bring curiosity.
Build methodological depth.
We welcome thoughtful inquiries about quantitative methodology and collaborative empirical research.
Prospective students
We welcome inquiries from researchers and students interested in psychometrics, structural equation modeling, interpretable machine learning, and methodological research for psychological and learning sciences. Introduce your research questions, methodological interests, and relevant preparation. Collaborative empirical projects are developed with substantive-area experts. Current supervision availability and formal admissions arrangements must be confirmed directly; no admissions cycle or funded opening is announced here.
Please contact the PI to confirm current supervision availability, admissions arrangements, and any funded opportunities.
Recommended preparation ↓Research collaborators
We are interested in substantive questions that expose limitations in existing measurement and modeling approaches. Tell us what you are trying to learn, how the data were collected, and where methodological uncertainty enters.
What to include
- Your research question and disciplinary context
- Relevant methodological interests or preparation
- A short description of the proposed collaboration
- Links to public work, if available
For prospective students
Recommended preparation
Preference may be given to candidates with coursework in one or more of the following areas, or with relevant research or programming experience.
Statistics
Probability theory, mathematical statistics, regression, categorical data analysis, causal inference, and time series analysis
Quantitative psychology
Measurement theory, latent variable modeling, structural equation modeling, item response theory, cognitive diagnostic models, multilevel modeling, and longitudinal analysis
Applied statistics across disciplines
Applied statistics in any substantive field, such as econometrics, biostatistics, or educational statistics
Computer science
Machine learning, deep learning, reinforcement learning, and computer programming
Research and programming experience
Relevant research and programming experience is also valued. In your inquiry, describe the questions you have studied, the methods or software you have used, and your own contributions to the work.
These are preferred areas of preparation; candidates are not expected to have completed every course listed. Please also check the applicable graduate program’s formal admission requirements.