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 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).
My work on Parameter Advising for transcript assembly has been accepted for an oral presentation at the European Student Council Symposium in Athens, Greece. In addition I will be attending ECCB in September to present a poster at the main meeting.
The Education and Internships Committee poster that I presented at ISMB in the Education COSI Track received the F1000 Outstanding Presentation Prize at ISMB. This is a great honor for the council and I hope it will improve the visibility of a vial program for our group. The poster focussed on our recent publication in PLOS Computational Biology which highlighted the process of the ISCB-SC Internships Program, I had mentioned it previously. The paper and poster are linked from internships.iscbsc.org. Thanks to the entire committee for making it such a great program.
I will be presenting my most recent work, titled “Automatically eliminating errors induced by suboptimal parameter choices in transcript assembly”, at the Machine Learning in Computational and Systems Biology (MLCSB) COSI at ISMB 2018 in Chicago. More details about the talk as well as slides will be posted before the meeting.
I will also be presenting the ISCB-SC Education and Internships Committee’s poster a the Education COSI meeting at ISMB. This is a highlight of our previously published paper in PLOS Computational Biology (see Publications > Service).
Soon after joining the Kingsford group I began talking with Guillaume Marçais about his work on minimizer schemes. This year I contributed to the most recent publication in this line of work which describes the asymptotic bounds for the densities of these schemes. This work was accepted for presentation at ISMB 2018 in Chicago, IL. A preprint of the manuscript is on bioRxiv (see Publications).
The book based on my dissertation written along with my PhD advisor John Kececioglu is now available! The work is a part of Springer’s Computational Biology series. The book contains a chapter that had not been previously published and updated results not previously in my dissertation.