IEEE Elevation to Senior Member
Congratulations to Dr. Fahad Saeed for his elevation to Senior Member of the IEEE. Only 9% of IEEE members have achieved Senior Member level. The Senior Member level is the highest grade to which a member of the IEEE may apply.
Graduate Teaching Effectiveness Award
Congratulations to Jason Johnson, this year's recipient of the All-University Graduate Teaching Effectiveness Award with the Department of Computer Science.
Study Engineering in China Summer Program
A special China program, one credit with tuition fee waive, for two weeks in China, starts now. All expenses within China are covered by Sichuan University in China. The program fee is $400 only for visa, insurance, and registration fees and you earn 1 credit for free. If you are interested, contact Dr. Dewei Qi immediately. Seats are limited and allocated on a first come first served basis. More information can be found here.
Bachelor of Science in Data Science
The Departments of Statistics and Computer Science announce the creation of a B.S. degree in Data Science beginning Fall 2015. Data Science is a rapidly evolving discipline and sits at the intersection of Statistics and Computer Science. This evolution has been driven by the exponential increase in processing power available and the ever-increasing amount of data now collected, stored and available in every facet of business, science and government. There is an ever-increasing need for big data, and going forward it is likely that most jobs for statistics graduates will include a big data component. It is also clear that many computer science undergraduate graduate job opportunities will involve big data as well. This B.S degree prepares practitioners in data science. For more information regarding the requirements of the B.S. in Data Science, please see the listing on the Statistics Department site.
Center for High Performance Computing and Big Data
The Department of Computer Science at Western Michigan University is pleased to announce the formation of the Center for High Performance Computing and Big Data. The Center’s co-directors will be Drs. Elise DeDoncker, John Kapenga and Fahad Saeed. The Center will support Big Data science projects using high performance computing resources from the High Performance Computational Science (HPCS) Lab facilities. These facilities include a high performance computation cluster installed in 2012 with funding from a $289,574 National Science Foundation Major Research Instrumentation (MRI) grant to support interdisciplinary projects in computational science and engineering.
National Science Foundation Cloud-Based Software Testing Grant
Dr. Zijiang Yang (PI) has been awarded a $65,559 National Science Foundation EAGER grant entitled “Systematic and Scalable Testing of Concurrent Software in the Cloud.” The objective of this research is to develop new algorithms and software tools to address the crucial problems of systematic and scalable testing of shared-memory concurrent software. The proposed methods, based on new symbolic execution algorithms and large-scale parallelization over clusters and the cloud, have the potential to achieve a super-linear speedup over the current state-of-the-art. If successful, this research will result in a new and practical software testing framework, which will be crucial in reducing the development cost for concurrent software, thereby leading to cheaper, more reliable, and more secure computer systems. NSF EAGER award supports exploratory work in its early stages on untested, but potentially transformative, research ideas or approaches. In addition, Dr. Yang has received a Google Computer Science Engagement Award, which gives him an unrestricted gift of $5,000 to support his teaching and research in Computer Science.
National Science Foundation High Performance Computing Big Data Grant
Dr. Fahad Saeed, assistant professor of computer science and electrical & computer engineering, was recently awarded a Research Initiation Initiative (CRII) research grant of US $171,341 from the National Science Foundation (NSF). The grant will support his research on high performance algorithms and architectures for Big Data. The research proposal entitled “HPC Solutions to Big NGS Data Compression” (Feb 2015 – Feb 2017) proposes to design and implement novel data-aware solutions for compression of large genomic data sets using high performance architectures and algorithms. Successful completion of this research will have significant impact on clinical as well as system biology labs and will move us one step closer to personal genomics era. This two-year pre-CAREER award was competitively awarded through NSF’s merit-review process and is supported by the NSF CCF Core program. Dr. Saeed is the sole PI on this grant.
Qatar Foundation Intelligent Transport Systems Grant
Drs. Ala Al-Fuqaha (WMU, PI), Elyes Ben Hamida (QMIC, Lead-PI) and Bharat Bhargava (Purdue University, PI) have been awarded a $900,000 Qatar Foundation grant to study intelligent transport systems (ITS). Through the use of wireless technologies, ITS systems will enable vehicles to autonomously communicate with other nearby vehicles or road infrastructures and thus, will have the potential to accelerate the deployment of a wide range of road safety and driver assistive applications. This innovative project aims at establishing a long term and multidisciplinary R&D efforts between Qatari and US research centers and universities, with the objective of designing, deploying and evaluating an Adaptive ITS Framework for the dynamic adaptation of the security and performance features based on changes in the ITS applications needs and context. The proposed framework and security models will be integrated in a standard compliant ITS platform, and a set of active road safety applications will be demonstrated in Doha city through small scale deployments.
National Science Foundation Adaptive Memory Resource Management Grant
Drs. Steve Carr (WMU, co-PI), Laura Brown (MTU, PI) and Zhenlin Wang (MTU, co-PI) have been awarded a $400,000 National Science Foundation grant entitled “Adaptive Memory Resource Management in a Data Center - A Transfer Learning Approach.” Cloud computing has become a dominant scalable computing platform for both online services and conventional data-intensive computing. By sharing computing resources among a large set of subscribers, a cloud computing data center (DC) provides a cost effective means to give users access to computational power and data storage that is not practical in an individual setting. To guarantee Quality of Service (QoS), a DC often has to over-commit its resources to meet the goal. This proposal focuses on the effective management of memory resources within a cloud computing DC using transfer learning.
National Science Foundation Cognitive Radio Grant
Drs. Ala Al-Fuqaha (WMU, co-PI), Bilal Khan (CUNY, PI) and Kirk Dombrowski (UNL, co-PI) have been awarded a $499,986 National Science Foundation grant entitled “Applying Behavioral-Ecological Network Models to Enhance Distributed Spectrum Access in Cognitive Radio.” In drawing the connection from the problem of resource-sharing in Cognitive Radio (CR), to models of solutions found within human/animal societies, this project evaluates the extent to which our models of patterns of co-use in biological systems can be profitably leveraged within the context of distributed uncoordinated CR societies to enable individuals and groups to maximize their utility. Of particular relevance to this endeavor is recent ethnographic research on foraging networks of indigenous peoples and human foragers, which has found social relations to be a critical context in which natural selection acts on resource use and co-use behaviors. These findings concerning human behavior lie at the forefront of anthropology, revealing the tensions between sharing networks and optimal strategies and altering our understanding of past human social evolution, and by extension, our vision of the future evolution of artificial CR societies.
National Science Foundation Genome Sequencing Grant
Prof. Fahad Saeed have been awarded $40,000 from the National Science Foundation (NSF) for developing high-performance solutions for Big genomic Data. This project deals with the design and development of high performance algorithms and implementations for aligning large number of genomes using innovative sampling and domain decomposition strategies. The proposed algorithms will be implemented on hybrid computing platforms consisting of multicore clusters, GPU's and FPGA’s.