Collaborative Research: BEGIN with Data Bits: Leveraging Atomic Linearity for Multi-Resolution Statistical Inference and Machine Learning Interpretability

Reliable methods for learning from complex data, central to the field of Artificial Intelligence (AI), are essential for scientific discovery and for decisions that affect national health, prosperity, and welfare. Modern studies often collect measurements on many interacting variables, but standard statistical methods may require simplifying assumptions that are difficult to verify and may miss important relationships in the data. This project will develop a new way to understand such relationships by studying data at the level of the binary digits used by computers to represent information. Working at this basic level will help researchers build tools that are more reliable, interpretable, and broadly applicable across many types of data. The results will support advances in areas such as neuroscience, genetics, engineering, economics, and other fields where scientists need to distinguish meaningful patterns from noise. The project will advance the progress of science by improving the foundations of data analysis, strengthening reproducibility in scientific research, and providing research training for graduate, undergraduate, and high school students. This project will focus on developing the Binary Expansion Group Intersection Network (BEGIN) as a framework for statistical learning from data bits. The framework will construct graphical models directly from binary representations of data and will use ideas from binary expansion and abelian group theory to study conditional dependence, which describes how variables are related after accounting for other variables. The project will establish theory and methodology for testing and modeling conditional independence using data bits. This foundation will then be used to develop bit-based methods for causal inference and interpretable machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2610629 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Kai Zhang | Institution: University of North Carolina at Chapel Hill, CHAPEL HILL, NC | Award Amount: $174,999 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2610629 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2610629.html