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 have developed probabilistic models for ribosome profiling datasets (link), which give a high-resolution snapshot of the distribution of ribosomes across a genome. From these data, we can extract information about local and global kinetics of translation.
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 have worked on statistical models that incorporate high-throughput structure probing datasets (link) that contain partial structure information to help improve secondary structure prediction. I have also worked on models (link) and algorithms (link) for more improving prediction of complex "pseudoknotted" structures.
Other interests include predicting regulatory modules for gene expression (link) and genetic variation as it relates to translation.
At Microsoft Research, I worked on sequence assembly algorithms for polyploid genomes (link). My undergrad work includes: (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.
I am generally interested in probabilistic models for generative processes or problems where domain information is useful. At Google, I worked on machine learning models for ad click-through rate prediction.