Foundations of Model Driven Discovery from Massive Data
Funded by the National Science Foundation’s TRIPODS program (Transdisciplinary Research in Principles of Data Science), this cross-disciplinary institute promotes collaborative research to foster the development and application of theory and methods of data science to create theoretical models governing systems or data-driven processes, and improve predictions.
Building on Brown’s strengths, the project brings together the disciplines of Mathematics, Statistics, and Theoretical Computer Science to define and refine the foundational landscape of the emerging area of data science. Through focused workshops, lectures, and mini-courses that cut across disciplinary boundaries, researchers and students at all levels work together in domain areas from neuroscience to climate modeling, from genomics to public policy.
The three-year project examines three research areas:
- Geometric and Topological Methods in Complex Data
- Causal and Model-Based Inference
- Data Analysis on Massive Graphs and Networks