Statistical Inference
Scalable estimation and approximation for models with latent quantities and complex dependence.
About Us
HETU Lab develops scalable statistical methods for high-dimensional and dependent data. We connect statistical theory, computation, and domain knowledge to study questions across disciplines.
Led by Yizhou (Jake) Cai in the Department of Mathematics at Texas State University, our work spans inference, latent-variable models, structural changes, and interdisciplinary data science.
If you are interested in joining our team, send us an email.
Research Areas
Statistical Methodology
High-dimensional & Dependent Data
Methods for spatial, spatiotemporal, ordinal, and other structured observations.
Change-point & Causal Methods
Identifying structural changes and studying causal questions in multivariate and evolving data.
Interdisciplinary Research
Public Health & Mental Health
Health-related information, social media, and changes in mental-health trajectories.
Finance & Insurance
Insurance claims, spatial risk, and dependence modeling.
Education
Collaborative statistical research on questions in education.
AI & Society
Generative AI adoption, visual-AI bias, and the use of AI in academic practice.