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.
Applied Machine Learning Research for Bioinformatics and Biomedicine
Marie desJardins, Patti Ordonez, and Shiming Yang (CSEE, UMBC CSEE), Matthias Gobbert (Math/Stat, UMBC), Steve Freeland (Biology, UMBC), Jim Fackler, Kathryn Holmes, and Christoph Lehmann (Johns Hopkins University)
We are studying scalable machine learning algorithms for complex application domains in biology, bioinformatics, and medicine. Areas of application include simulations calcium transfer in heart cells (joint work with Matthias Gobbert (Math/Stat)), modeling the evolution of the amino acid alphabet (joint work with Steve Freeland (Biology)), and analyzing vital signs data for ICU patients (joint work with Jim Fackler, Kathryn Holmes, and Christoph Lehmann at Johns Hopkins).