Research Project Title:
Inferring Latent Structure in Graphical Models with Binomial Random Variables
abstract:Probabilistic graphical models are used to capture phenomena that exhibit some form of uncertainty. Often, using such a framework allows for tractable inference about the parameters that exhibit this uncertainty. In some applications, the inference task of interest is with respect to the relationship between the parameters (or structure of the graphical model used). Such problems of model learning will be our focus in this project, where we develop an algorithm targeted at inferring the latent structure of a gene regulatory network from noisy cellular RNA measurements. We model this problem as a problem of inferring the latent structure of a graphical model with binomial random variables.
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.