Research and Awards

In an ever-changing field, our faculty are also leading researchers, helping to define the future of technology through their work. As a student, you will learn from faculty who are versed in the future of computer science. 

Meet our researchers

  • Faculty research spotlights

    Dr. Shameek Bhattacharjee

    Shameek Bhattacharjee is an assistant professor at the Department of Computer Science. He received his Ph.D., and M.S. degrees in computer engineering from University of Central Florida in 2015 and 2011, respectively, and a B. Tech degree in Information Technology from West Bengal University of Technology (Techno India College), India in 2009. His research interests span across broad areas of cyber and information security and networking, with a special focus on cyber physical systems/internet of things, smart connected communities, and next generation wireless networks and data driven security analytics.




    Dr. Guan Yue Hong

    Guan Yue Hong is an associate professor of computer science at Western Michigan University. She received her Ph.D. in software engineering from National University of Singapore. Her research interests include reliable and trustworthy AI, novel machine learning paradigms, and smart cyber-physical-human systems.  She received several grants and awards in support of her scholarly activities. She has published over 70 papers in leading international journals and conferences, e.g. IEEE Trans. Affective Computing, IEEE Trans. Consumer Electronics, and IEEE Trans. IT in Biomedicine. Her published papers have attracted over a thousand scientific citations.



    Dr. Elise de Doncker

    Elise de Doncker is a professor of computer science at WMU. She obtained her doctoral degree in mathematics at the Katholieke Universiteit Leuven (KUL), Belgium. Her research revolves around algorithm design and analysis, particularly in scientific computing and numerical integration, and targeted toward high performance computations and architectures. To date (7/24/20), the Quadpack integration package, developed when she was a student (with R. Piessens, D.K. Kahaner and C.W. Uberhuber) has 1,356,922 downloads from, and the Quadpack book was cited 1218 times according to Google Scholar. She also pioneered the ParInt package for multivariate integration (with A. Gupta, A. Genz and R. Zanny) for cluster and distributed integration. Perhaps in Renaissance style she later branched out to other fields including neural networks, agent-based simulations (of epidemics/pandemics), sentiment analysis, human behavior modeling, and bioinformatics.

    Dr. Alvis Fong

    Fong holds four degrees in CS and EE from three universities: Imperial College London, University of Oxford, and University of Auckland. His research activities revolve around next-generation artificial intelligence (NG-AI). Specific focus areas include data-driven and goal-informed knowledge discovery, ontological knowledge representation and reasoning, and applied machine learning. His publications and intellectual property contributions include two books, 13 book sections, 100 journal papers, 100 conference papers, and two international patents. Archival journals that carry his work include IEEE T-KDE, IEEE T-AC, IEEE T-II, IEEE T-EC, and several other IEEE Transactions titles. He has been an Associate Editor of IEEE T-CE since 2013. He has recently served as a guest editor of IEEE CE Magazine’s special section on Machine Learning for End Consumers, which will appear in 2020 (DOI 10.1109/MCE.2020.2986934). Dr. Fong is a Fellow of IET, European Engineer (Eur Ing), and Chartered Engineer (CEng).

  • Awards

    Dr. Avlis Fong receives a DOE award

    Dr. Alvis Fong is a co-PI on a $2M DOE award to advance technology in zero-emission vehicles. Their project aims to provide real-world evidence to demonstrate how advanced infrastructure technology can improve energy savings, resilience, and safety. Key technical challenges include real-time fusion of infrastructure data and development of robust and computationally light AI algorithms. The compiled code will be implemented directly on a test vehicle for fast prototyping and evaluation.

    Dr. Bhattacharjee receives new NSF award

    Dr. Shameek Bhattarcharjee recently received a new research grant from the National Science Foundation. The grant is entitled TAURUS: Towards a Unified Robust and Secure Data Driven Approach for Attack Detection in Smart Living. 


    The smart living vision aims to improve human quality of life and social well-being. Cornerstone cyber-physical systems (CPS) for realizing the smart living vision include smart grids, smart homes, smart transportation, and connected healthcare; which generate huge volumes of time series data through sensor-actuator devices, the so-called Internet of Things (IoT). Such data may be a target of low-profile stealthy attacks that hide behind the high randomness of benign smart living IoT data trends, making it hard to detect through traditional cybersecurity approaches.

    The intertwined dependence on data analytics and artificial intelligence, civilian impact of wrong decisions, competitive economic motivations, make the underlying IoT and CPS domains extremely vulnerable to data integrity and availability attacks, thwarting the accuracy of analytics dependent operations of smart living applications. This NSF funded project aims to create tremendous impact by developing a foundational science of data driven and AI-inspired security approaches for emerging IoT-based applications in smart living. It addresses stealthy attacks from both cyber and physical exploits that hide behind high randomness of data, caused due to human behavioral differences, and codifies a unified security framework at community scale under various attack types and strengths. Thus, the TAURUS project will drastically reduce the number of concurrently running security solutions and their cross coordination, to improve security in smart living IoT applications. Additionally, this project will recruit and mentor undergraduate and graduate students, including women and underrepresented minority students, as well as train K-12 students through various schemes at partner institutions.

    Fong, Bhattacharjee, Gupta and Carr Receive NSF Funding

    Drs. Fong, Bhattacharjee, Gupta and Carr recently received a National Science Foundation Grant entitled Module Experiential Learning for Safe, Secure and Reliable AI. This grant creates curriculum materials to teach artificial intelligence and multiple levels of the CS curriculum.   


    From self-driving vehicles to smart digital personal assistants and real-time multilingual translators, applications of artificial intelligence (AI) have made headlines in recent years. Indeed, there seems to be universal agreement among influential scientists, commentators, and politicians that winning the “AI race” will be tremendously important in ensuring continued national security, competitiveness, and prosperity. As AI increasingly permeates every facet of our daily lives, we begin to observe reported cases of AI-related failures and misadventures. We are now at a critical juncture when there is an urgent need to ensure that current and future scientists who advance AI, as well as practitioners who use AI, understand the limitations of AI and how to develop robust and dependable AI.  In the short term, this pilot aims to integrate core literacy and advanced skills at the intersection of Secure, Safe, Reliable (SSR) Computing, High Performance Computing (HPC), and AI into the Nation’s educational curriculum and training materials that will prepare faculty, undergraduate, and graduate students at institutions with relatively low rates of advanced cyberinfrastructure (CI) adoption for large-scale secured data analytics. The long-term goals are to contribute to a pipeline of SSR AI-minded CI workforce and a self-sustaining advanced CI ecosystem.  

    AI systems can be fooled to perform undesirably, exhibit biases or abusive behaviors. When AI algorithms are parallelized on HPC CI, such misbehaviors, biases, and uncertainty can multiply to obscure the root causes. SSR computing techniques can mitigate these problems. Inspired by authoritative sources, e.g. Open AI, Partnership on AI, this pilot aims to inform curriculum and develop materials to educate computer science (CS) students from the outset and will in turn improve the practice of AI that runs on advanced CI. Intensive, multi-faceted, modular, experiential learning units will be designed to upgrade the skills of current and future CI users rapidly, so they can apply their new skills to their tasks. The loosely coupled modules can be integrated into existing classes, including elementary CS classes taken by non-CS STEM students. Students will participate in research activities, which will train next generation interdisciplinary scientists at WMU, including many from underrepresented groups. Other second-tier universities, HBCU, WMU’s rural underserved locations, and urban community colleges will also participate in the pilot. Using a collective impact plan, a group of multi-discipline, public-private-sector experts will provide guidance and participate in train-the-trainer activities to multiply the effect. Lessons learned and best practices will be codified into blueprints for reusability and widespread future adoption across STEM disciplines. 

    Research Grant Award - Muaaz Gul Awan

    Dear Muaaz, Congratulations on your great success in being awarded a Graduate Student Research Grant. We are very proud of your accomplishments and wish you all the best as you continue your educational career here at Western Michigan University.

    Research Grant Award - Mohammed Aledhari

    Dear Mohammed, Congratulations on your great success in being awarded a Graduate Student Research Grant. We are very proud of your accomplishments and wish you all the best as you continue your educational career here at Western Michigan University.

    Dr. Fahad Saeed Receives Prestigious NSF CAREER Award

    Dr. Fahad Saeed, a Western Michigan University assistant professor in the Departments of Computer Science and Electrical and Computer Engineering, was named a recipient of the highly prestigious, 5-year, $500,000 CAREER award from the National Science Foundation (NSF). Saeed’s grant will be used to lay a foundation for fast algorithmic and high performance computing solutions suitable for analyzing big proteogenomics data sets. He plans to involve both undergraduate and graduate students in the research, as well as develop an outreach program for K-12 students. For more information, see

    NIH Research Grant on Developing Core Algorithms & Techniques for Big Proteomics Data

    Dr. Fahad Saeed, assistant professor of computer science and electrical and computer engineering, recently was awarded a research grant of US $418,533 from the National Institutes of Health (NIH). The goal of the funded research is to develop core algorithms (sets of rules in computerized solutions) and techniques to enable scalable, efficient and high-performance computing solutions for big mass spectrometry data based proteomics. The proposed research is expected to have a significant impact in systems biology research since the proposed techniques will allow scientists to perform much more complex and accurate proteomics analysis than was previously possible. The findings of such analysis can lead to prevention, diagnosis and treatment of diseases with genetic predisposition such as cancer, obesity, diabetes, heart disease and mental illness. The efficiency and portability of the proposed techniques will have seminal impact in precision and personal medicine. Dr. Steve Carr, Chair of the Department of Computer Science, states, “The grant obtained by Dr. Saeed is significant for Western Michigan University. This award confirms that Dr. Saeed’s work is at the cutting edge of research in computing solutions for biological problems. We are all proud of Dr. Saeed and feel very fortunate that he is having such a significant impact for WMU.”

    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

    Elise DeDoncker is PI at WMU, with Diana Prieto at Johns Hopkins University (JHU), and Rajib Paul at the University of North Carolina at Charlotte (UNCC), 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 is 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

    Dr. Fahad 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. 

    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.