Computational Phylogenetics

I am currently a PhD candidate advised by Tandy Warnow and Bill Gropp. My thesis research focuses on parallel algorithms for building evolutionary trees from large genomic datasets. I have also worked on other practical issues related to analyzing large phylogenomic datasets, for example, the impact of estimation error and missing data.

Peer-reviewed Journal Publications

  1. E. Molloy and T. Warnow. 2019. TreeMerge: A new method for improving the scalability of species tree estimation methods. Bioinformatics (Special Issue for ISMB 2019) 35(14), i417–i426. [html] [code] [data]
  2. E. Molloy and T. Warnow. 2019. Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge. Algorithms for Molecular Biology (Special Issue for RECOMB-CG 2018), in press. [html] [code] [data]
  3. M. Nute, J. Chou, E. Molloy, T. Warnow. 2018. The Performance of Coalescent-Based Species Tree Estimation Methods under Models of Missing Data. BMC Genomics 19 (Special Issue for RECOMB-CG 2017), Article 286, 22 pages. [html] [data]
  4. S. Christensen, E. Molloy, P. Vachaspati, T. Warnow. 2018. OCTAL: Optimal completion of gene trees in polynomial time. Algorithms for Molecular Biology 13 (Special Issue for WABI 2017), Article 6, 18 pages. [html] [code] [data]
  5. E. Molloy and T. Warnow. 2018. To include or not to include: The Impact of Gene Filtering on Species Tree Estimation Methods. Systematic Biology 67(2), 285-303. [html] [data]

Peer-reviewed Conference Proceedings

  1. S. Christensen, E. Molloy, P. Vachaspati, T. Warnow. 2019. TRACTION: Fast non-parametric improvement of estimated gene trees. Accepted to the 19th International Workshop on Algorithms and Bioinformatics (WABI 2019). [code]
  2. T. Le, A. Sy, E. Molloy, Q. Zhang, S. Rao, T. Warnow. 2019. Using INC within Divide-and-Conquer Phylogeny Estimation. In Proceedings of the 6th International Conference on Algorithms for Computational Biology (AlCoB 2019), Springer Lecture Notes in Computer Science, Vol. 11488, 167-178. [html] [code] [data]
  3. E. Molloy and T. Warnow. 2018. NJMerge: A Generic Technique for Scaling Phylogeny Estimation Methods and Its Application to Species Trees. In Proceedings of the 16th RECOMB International Conference on Comparative Genomics (RECOMB-CG 2018), Springer Lecture Notes in Computer Science, Vol. 11183, 260-276. [html] [code] [data]
  4. S. Christensen, E. Molloy, P. Vachaspati, T. Warnow. 2017. Optimal completion of incomplete gene trees in polynomial time using OCTAL. In Proceedings of the 17th International Workshop on Algorithms and Bioinformatics (WABI 2017), Leibniz International Proceedings in Informatics, Vol. 88, Article 27, 14 pages. [html] [code] [data]

Invited Book Chapters

  1. E. Molloy and T. Warnow. 2019. Large-scale Species Tree Estimation. Preprint available on arXiv. [html]

Moral Judgement / Decision Making

During my first year of graduate school, I worked in Aron Barbey's Decision Neuroscience Laboratory as part of the NSF Neuroengineering IGERT program. The paper below is related to designing ecologically valid moral judgement tasks that can be performed in the context of a neuroimaging study.

Peer-reviewed Journal Publications

  1. M. Kruepke, E. Molloy, K.W. Bresin, A.K. Barbey, E. Verona. 2017. A Brief Assessment Tool for Investigating Facets of Moral Judgment from Realistic Moral Vignettes. Behavior Research Methods 50(3), 922-936. [html] [code] [data]

Functional Neuroimaging

Before graduate school, I was a neuroimaging researcher, mentored by Professor Rasmus Birn, at the Health Emotions Research Institute. During that time, I evaluated and addressed critical methodological problems in resting-state functional MRI, including systematic image artifacts and subject test-retest reliability.

Peer-reviewed Journal Publications

  1. R. Patriat, E. Molloy, R.M. Birn. 2015. Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies. Brain Connectivity 5(9), 582-595. [html]
  2. E. Molloy, M.E. Meyerand, R.M. Birn. 2014. The influence of spatial resolution and smoothing on the detectability of resting-state and task fMRI. Neuroimage 86, 221-230. [html]
  3. R.M. Birn, M.D. Cornejo, E. Molloy, R. Patriat, T.B. Meier, G.R. Kirk, V.A. Nair, M.E. Meyerand, V. Prabhakran. 2014. The Influence of Physiological Noise Correction on Test-Retest Reliability of Resting-State Functional Connectivity. Brain Connectivity 4(7), 511-522. [html]
  4. R.M. Birn, E. Molloy, R. Patriat, T. Parker, T.B. Meier, G.R. Kirk, V.A. Nair, M.E. Meyerand, V. Prabhakran. 2013. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83, 550-558. [html] [code]
  5. R. Patriat, E. Molloy, T.B. Meier, G.R. Kirk, V.A. Nair, M.E. Meyerand, V. Prabhakran, R.M. Birn. 2013. The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated. Neuroimage 78, 463-473. [html] [code]
  6. C.A. Burghy, D.E. Stodola, P.L. Ruttle, E. Molloy, J.M. Armstrong, J.A. Oler, M.E. Fox, A.S. Hayes, N.H. Kalin, M.J. Essex, R.J. Davidson, R.M. Birn. 2012. Developmental pathways to amygdala-prefrontal function and internalizing symptoms in adolescence. Nature Neuroscience 15(12), 1736-1741. [html]