Research Awards



Tenth Annual WMU Research, Creative Activities Poster and Performance Day

Graduate students Abduljaleel Al-Hasnawi and Ahmed Almulihi won awards at the Tenth Annual WMU Research, Creative Activities Poster and Performance Day yesterday. There were 40 graduate student presenters and 19 students received recognition awards for their high scores by the judges.

NSF Research Influenza Modeling

Dr. Elise DeDoncker is a Co-PI with Diana Prieto (IME) PI, and Rajib Paul (STAT) Co-PI on a grant from the NSF Program on Service, Manufacturing, and Operations Research, in the Division of Civil, Mechanical and Manufacturing Innovation (NSF CMMI). The award entitled "Sampling Criteria for Monitoring Influenza Emergencies Under Constrained Testing Capabilities". The grant runs from 09/01/15 for 36 months.

NSF Research Experiences for Undergraduates

Prof. Saeed has been awarded US$ 16,000 as Research Experience for Undergraduate (REU) supplement which is part of NSF grant CRII CCF-1464268. The undergraduate students working on this REU project will closely work with Dr. Saeed and his graduate students for developing several components of importance for the overall success of the ongoing HPC big data research efforts. The specific activities of the undergraduate participants will focus on creating graphical user interfaces for the proposed HPC algorithms and porting the application code on multiple architectures such as many-cores and GPU’s. They will also assist graduate students in implementing applications that can utilize the genome-specific network protocols for efficient transmission of big genomic data sets. Undergraduate students wishing to work in parallel computing and data science (PCDS) lab should send an email of interest and CV to Prof. Saeed at <>

NSF Visualization and Analysis for C Code Security Grant

Dr. Steve Carr (WMU, PI), James Yang (WMU, co-PI), Jean Mayo (MTU, PI) and Ching-Kuang Shene (MTU, co-PI) have been awarded a $300,000 National Science Foundation grant ($169K WMU, $131K MTU) entitled “VACCS - Visualization and Analysis for C Code Security.” The grant will develop an educational system which will utilize static and dynamic program analysis to help detect potential security vulnerabilities and use visualization to help teach programmers about the potential errors in their code. The goal of this project is to help students learn by seeing what is wrong with programs rather than just having it explained in words.

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.