Research Project Title:
Inferring Structure in Gaussian Graphical Models from Noisy Observations
abstract:In this project, we develop an algorithm targeted at inferring the latent structure of directed acyclic Gaussian graphical models from observations through a noise function. This algorithm has direct applications in computational biology, where inferring the structure of Gene Regulatory Networks, i.e. the causal relationship between the genes expressed in each cell, is of great importance. We provide guarantees on the consistency of our algorithm, and evaluate its performance in controlled simulations. Moreover, we display its operation on a single-cell RNA-seq data-set.
I'd like to participate in a research project where I hold a great deal of the responsibility, and I think going through this program in this lab will provide the necessary mentorship for me to be successful in doing that.