UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available after Summer 2014.
Currently, the 84-node cluster tara still operates independently,
until it becomes part of maya at the end of Summer 2014.
Please see the 2013 Resources Pages under the Resources tab for tara information.
Testing Species Tree Inference Methods
Kevin Omland, Biological Sciences
Matthias K. Gobbert, Mathematics and Statistics, UMBC, faculty
Andrew Li, Mathematics and Statistics, undergraduate student, UBM grant
Leila Bahmani, Biological Sciences, undergraduate student, UBM grant
Frode Jacobsen, Biological Sciences, postdoc
John Malloy, Biological Sciences, undergraduate student
Tylynn Pettrey, Mathematics and Statistics, undergraduate student
During the last twenty years there has been a revolution in the field of
phylogeny (the study of evolutionary trees) made possible by genomics in
biology and by the development of efficient search algorithms in
mathematics and statistics. More recently, collaborative teams of
statisticians and biologists have developed specific computationally
intensive methods for determining evolutionary relationships. Many new
"species tree inference" methods have been proposed (e.g., programs
going under the acronyms BEST, BUCKy). Many of these are Bayesian MCMC
methods that are very computationally intense. UBM undergraduate
students will work with Dr. Omland and Dr. Gobbert to develop efficient
search strategies to test these different methods using the UMBC High
Performance Computing Facility.