Below you will find brief descriptions of some of the projects that have relied on the computational capacity of the cluster:

Business Administration
Professor Robert Nauss, College of Business Administration, has an ongoing investigation into solving large-scale generalized assignment problems. He is using his own program (parallelized by the cluster-affiliated programming staff) for this research. Professor Nauss will be using the cluster to solve the Single Source Capacitated Facility Location Problem. So far, this project has resulted in:

  • 3 conference presentations

Several researchers from the Kellogg Lab are conducting research to support the goals of the Lab. They are using the programs MrBayes and PAUP* on the cluster. So far, this project has resulted in:

  • Presentation at the Comparative Plant Genomic Keystone meeting (Taos, NM).
  • Presentation at the NSF-funded Evolution of Gene Regulation meeting (Eugene, OR).

Center for Neurodynamics (Biology, Physics, Optometry)
Researchers from the Center for Neurodynamics have used the cluster to support the Center's research goals. The researchers used their own programs to do this research. Results froom the Center so far include:

  • 1 journal publication

Professor Chung Wong, Department of Chemistry, has been using the cluster for the following projects:

  • Simulate the laser desorption of peptide analyte from MALDI matrices. This has resulted in 1 publication.
  • Develop an improved docking algorithm to help identify anti-cancer drugs targeting protein kinases.
  • Develop an improved Quantum Mechanics/Molecular Mechanics/Poisson-Boltzmann model for estimating the binding affinity between drug candidates and their protein targets.

Computer Science
Assistant Professor Martin Pelikan, Department of Math and Computer Science, has been using the cluster for the following:

  • Evolutionary Computation and Machine Learning

The Missouri Estimation of Distribution Algorithms Laboratory (MEDAL) focuses on the design, enhancement, analysis, and applications of estimation of distribution algorithms (EDAs), which represent a powerful class of stochastic optimization techniques inspired by evolutionary computation and machine learning. Besides EDAs, MEDAL's research interests cover other branches of genetic and evolutionary computation, learning classifier systems, bioinformatics, and machine learning.

Most research projects at MEDAL require extensive computational resources to perform tens of thousands to hundreds of thousands independent runs on different problem instances. To facilitate the high demand for
computational resources, MEDAL performs most of its computational tasks in parallel on the Beowulf cluster.

MEDAL is supported by the National Science Foundation, the Air Force Office of Scientific Research, and the Air Force Materiel Command.