Cristina Pop

 

Regulation of Translation

The process of translation, whereby RNA is converted to protein, is an essential step in protein expression, but our understanding of the regulatory mechanisms at this stage is limited. I develop probabilistic models for ribosome profiling datasets, which give a high-resolution snapshot of the distribution of ribosomes across a genome. From these data, we extract information about local and global kinetics of translation.

 
RNA Folding

The structure of RNA informs many different cellular processes. Given a sequence, we'd like to predict the set of base pairs formed under a certain energy model, while maintaining low algorithmic complexity, high generality (in the space of potential structures formed), and high accuracy. I work on statistical models that incorporate various experimental data by training on a set of known structures. I have also worked on models and algorithms for more complex "pseudoknotted" structures.

 
Other Biology Projects

I have worked on predicting regulatory modules for gene expression and am also interested in genetic variation as it relates to translation. During an internship at Microsoft Research, I worked on sequence assembly algorithms for polyploid genomes.

During my undergrad, I worked on: (1) algorithms for initial conditions for simulations of biological processes described by hybrid systems (pdf), and (2) a review of DNA sequencing using biological and synthetic nanopores.

 
Other Machine Learning Projects

I am generally interested in probabilistic models for generative processes or problems where domain information is useful, but have worked with other machine learning tools. During an internship at Google, I worked on machine learning models for ad click-through rate prediction.