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About our Work

• Phylogenetic trees, also known as evolutionary trees, give a graphical representation of the evolutionary
  history relating a set of species (or, in some cases, a set of gene sequences, of languages, etc.). The
  estimation of this evolutionary history is fascinating and provides key insights into biological processes.


• From a computational perspective, phylogeny estimation is challenging, because the best methods involve
  attempts to solve NP-hard optimization problems. While many optimization problems seem reasonably well
  analyzed for moderate-sized datasets using existing methods, very large datasets require new techniques.


• From a statistical perspective, understanding how well phylogeny estimation methods work involves posing
  stochastic models of how the taxa (meaning species, languages, etc.) evolved. This in turn requires that we
  consider events like substitutions and indels, but also more complicated phenomena, like horizontal gene
  transfers, hybrid speciations, genome rearrangements, fissions and fusions of chromosomes, etc., all of which
  make the estimation of evolutionary histories much more complicated. Some of these processes also require
  the use of networks rather than trees for graphical models.


• Our group develops new methods that can produce highly accurate trees and phylogenetic networks for very
  large datasets, with the goal being the accurate estimation of trees for hundreds of thousands of DNA
  sequences. We also have an active interest in designing methods for co-estimating alignments and trees, for
  estimating evolutionary histories of natural languages, and for detecting and reconstructing reticulate evolution.


• As such, state-of-the-art methods in computational phylogenetics draw on methods from a wide array of fields:
  mathematics, algorithm design, theoretical computer science, experimental algorithmics, parallel programming,
  statistics, artificial intelligence, and machine learning. Moreover, the field offers opportunities for work in other
  areas such as data visualization and historical linguistics. Modern research in phylogenetics is an active and
  highly-interdisciplinary endeavor.


• We work with biologists and computer scientists around the world to develop sequence alignment and
  phylogeny estimation methods; some of these are listed here.

How to Join

• If you'd like to learn more about our work, you'll want to check out our resources page for relevant tutorials,
  presentations, and courses here at UT. You can learn about what projects we're working on, read some of
  our publications, and sit in on our weekly journal club. If you are interested in joining our lab, please read
  this note and feel free to contact one of us.


• Our lab is part of the Center for Computational Biology and Bioinformatics (CCBB), which provides research
  support and opportunities for students, postdoctoral fellows, and faculty interested in the use of computational
  approaches to solving biological problems. We are also affiliated with several other research centers on campus.
  There are no specific degree programs in computational biology, but computational biology tracks are available
  in several degree programs; doctoral students have the opportunity to work with participating faculty from
  departments throughout UT.