Large-scale interaction networks naturally arise in many modern scientific applications. For example, in biochemistry and systems biology, amino acids in different locations of the protein sequence interact, while in computational neuroscience, connectivity networks amongst neurons in the brain naturally trigger responses to particular stimuli. This project will develop reliable and scalable algorithms for learning the underlying interaction network amongst many nodes. Due to both the scale, complexity, and the changing data technologies in the applications described above, the solutions to the challenges addressed in this project will lead both to the development of novel theory and methodology, and the implementation of new algorithms for the application domains.
The goal of the project is to address the challenge of estimation, inference and testing for large-scale network models. Given the size of the networks generated, this project presents a number of computational and statistical challenges the PI will address by focusing on two methodologies: (i) multivariate time series models; (ii) directed graphical models. The PI's prior work has developed new theory and methodology both for large-scale non-linear time series models and directed graphical models. This prior work points to a number of significant open challenges for both methodologies that this project will. These challenges include: (i) lack of sample size/statistical resources for learning complicated dependence structures; (ii) computational challenges due to non-convexity and large search-spaces for dependence models; (iii) incorporating domain knowledge and scientific experiments into the estimation methodologies; and (iv) exploiting learned networks for hypothesis testing, inference, and parameter estimation. This project will address these challenges and these contributions will lead to the development of new methods for network 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.
