My research interest are broadly in algorithm design and analysis, and I take inspiration from biological problems. Many times this not only leads to an interesting algorithmic result, but a useful biological tool (see Software).
I am currently an Assistant Professor in the Computer Science Department at the University of Texas at El Paso (UTEP). More information on my research group can be found at deblasiolab.org.
Previously I was a Lane Fellow in the Computational Biology Department at Carnegie Mellon University working with Carl Kingsford.
I was previously a PhD student in the Computer Science Department at the University of Arizona working with John Kececioglu and a student in the CS Department Department at the University of Central Florida working with Shaojie Zhang.
In the past my work has focused mainly on multiple sequence alignment problems. Most recently I worked on improving accuracy of protein multiple sequence alignments. Multiple sequence alignment is a fundamental step in bioinformatics, but the problem is NP-complete. Because of the importance of the result and complexity of the multiple sequence alignment problem many algorithms exist to find high quality alignments in practice. Each of these algorithms has a large number of tunable parameters that can greatly affect the quality of the computed alignment. Most users rely on the default parameter choices, which produce the best alignments on average, but produce poor alignments for some inputs. We developed a process called parameter advising which selects parameter choices that produces a high quality alignment for the input. To accomplish this candidate alignments are produced using each of the parameter choices in an advising set, the accuracy of these candidate alignments is then estimated using an advising estimator, the candidate alignment with the highest estimated accuracy is then selected for the user. To estimate the alignment accuracy we developed Facet (Feature-based accuracy estimator) which is a linear combination of efficiently-computable feature functions. We have found that learning an optimal advisor (selecting both the estimator coefficients and the set of parameter choices) is NP-complete. We expanded this result to show that finding the estimator coefficients or the estimator set independently is also NP-complete. In practice, we have methods to find close-to optimal advisors. We are working on ways to improve the accuracy of these parameter advisors.
I have also worked on improving the memory consumption of secondary structure conscious RNA multiple sequence alignment (see PMFastR) and high throughput phylogeny filtering (see SiClE).