With the combined resources of the Russ College of Engineering and Technology’s School of Electrical Engineering and Computer Science and our dedicated lab spaces, the researchers at our center conduct basic research in facilities dedicated to bioinformatics, immersive learning technologies, and high-performance computing studies.
Bioinformatics Laboratory
The Bioinformatics Laboratory at Ohio University is a testing ground for new computational methods to help answer research questions in medicine, agriculture, exercise physiology through the study of genetic and biological data. Our bioinformatics research involves five faculty from the School of Electrical Engineering and Computer Science and research projects in many areas of computational bioscience, including computational regulatory genomics, functional genomics, proteomics, image analysis, data mining, algorithm design and analysis, genetic algorithms and genetic simulators.
Collaborating Faculty and Institutions
Faculty in other units at Ohio University (such as the Edison Biotechnology Institute, the Genomics Facility, the Department of Environmental and Plant Biology, the Department of Biomedical Sciences, the Biomedical Engineering Program, the Department of Industrial and Systems Engineering, and the Department of Biological Sciences) work with the Bioinformatics Laboratory to solve life sciences research problems. Our bioinformatics researchers also collaborate with individuals at research institutes, labs and organizations around the country, including the National Human Genome Research Institute, the Air Force Research Lab, Ohio State University, the Ohio Agricultural Research and Development Center, the Ohio Supercomputer Center, Harvard University, Bowling Green State University, University of Kentucky, and Vanderbilt University.
Resources
Our lab provides state-of-the-art computational resources, featuring a 5-node cluster computer that has on each node 32GB RAM, 8 cores, 2TB hard disk space (a RAID5 array) and a dual-channel, gigabit ethernet. Staff support is provided for maintenance and upgrades for the cluster computer.
Virtual Immersive Technologies and Arts for Learning Lab
The Virtual Immersive Technologies and Arts for Learning Lab was founded in 2006 to create immersive virtual environments for teaching, learning, and training purposes. Led by Chang Liu, our researchers and graduate students in this lab collaborate with educators and subject-matter specialists to develop new technologies for learning a host of subjects, from nutrition to water quality to personal finance, in three-dimensional virtual environments.
For more info., contact VITAL Lab Director Chang Liu at 740.593.1249 , or visit vital.cs.ohio.edu .
Parallel and Distributed Processing Laboratory (PPL)
The Parallel and Distributed Processing Laboratory (PPL) provides the capability to design, create and test algorithms and computing systems for processing enormous amounts of data, such as genetic and biological data gathered in sequencing and disease analysis.
Ongoing investigations for researchers in the PPL include:
- Design and implementation of bioinformatics algorithms for discovery of functional elements in genomic sequences with a special emphasis on high-performance and scalable computing. One of the current areas of research is the development and implementation of algorithms for word-space enumeration of genomic sequences utilizing multiprocessor/multicore architectures, clusters-of-workstations, and GPGPU's
- Research and design of quality-of-service based operating system resource management components with a special focus on symmetric and asymmetric multi-processor and multi-core support. We have designed and implemented a number of resource management extensions to the Linux kernel that are specifically targeted at high-end server systems and mixed-critical application environments. Another important application for our research is temporal isolation of multiple guest operating systems in operating system virtualization.
- Design and development of air traffic control systems using COTS SIMD architectures. We design and develop core air-traffic control algorithms utilizing ClearSpeed COTS SIMD co-processors. The main goal of this research is to demonstrate that COTS SIMD board can be used to solve the air-traffic problem at a much lower cost than traditional multi-processor systems.