We are pleased to announce that Xinwei Shen, Assistant Professor of Statistics, has received grant funding support from the Royalty Research Fund (RRF) for her proposal “Universal Generative Models”.
Generative modeling has become central to modern statistics and artificial intelligence, enabling full distributional learning and uncertainty quantification. However, existing generative models are primarily designed for continuous outputs to accommodate gradient-based training, limiting their applicability to real-world data that frequently include discrete, categorical, ranking, or mixed-type variables. Such data arise widely in ecology (species co-occurrence), biomedicine (mixed clinical records), climate science (continuous measurements with event indicators), sports rankings, and natural language processing.
This project develops universal engression – a unified statistical framework for conditional distribution learning that handles arbitrary data types while remaining computationally efficient and statistically principled.
Congratulations to Xinwei on her success!
Shen Receives Royalty Research Fund (RRF) Award
