Assessing the quality and completeness of transcriptome assemblies is a challenge. For this problem, we developed a measure of gene assembly known as the “ortholog hit ratio.” First we associate each assembled gene with its closest match in a related organism. Then we compare the length of the matching region to the total length of the related gene. (Comparing the length of only the matching region ignores untranslated regions on the ends that are not considered part of the gene.
When working with sequencing datasets of ecological interest, an interesting problem is how to tease out the genetic diversity present in the population being sequenced. Usually, assembly software simply aligns the short read sequences, and determines the consensus sequence based on the majority vote of each position. However, we may wish to seperately assemble each haplotype (version) of each gene. We formulate this as a graph problem, where short reads that overlap are considered nodes in a graph that share an edge if they should go in different haplotypes.
Some extended family got married in Jamaica, I did some photography for them.
I did this project with Mike Olson, a Notre Dame graduate student. (He’s narrating the video.) We studied the proper design of user interfaces in this class. Although we mostly focused on graphical interfaces, we also learned how to interface with motion-based systems such as the Nintendo Wii remote and Balance Board. For our final project, we wanted to design an interactive browser for the Microsoft Surface, a multitouch based coffeetable like device.
This class was very interesting; we studied some of the basics of online algorithms as well as some applications to computational finance. Perhaps the most intriguing parts (for someone already familiar with online algorithms) were what we learned about classic computational finance, such as Black-Scholes-Merton. This “project” is a review of the main result of an interesting paper, entitled “Making Decisions in the Face of Uncertainty: How to Pick a Winner Almost Every Time,” by Awerbuch et al.
Much of the research I performed during my Master’s was on the newsvendor problem, which deals with attempting to guess the demand for short-lived products so as to maximize profits. Unfortunately, data about newspaper sales is tough to come by. So, why not use an online equivalent? This project aims at predicting the number of comments a Slashdot story will generate given text which appears in the summary. However, since we are interested in looking at the number of comments as a demand for a product to be sold, we don’t stop there.
