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College of Computing and Software Engineering 2021-2022 Projects

Click here to return to the main project listings page.  Questions: Email our@kennesaw.edu.

  • 2021-2022 First-Year Scholar: Richard Borowski, Computer Science

    • Recent and rapid advances in Artificial Intelligence (AI), particularly in the form of deep neural networks, has opened many new possibilities, but it has also brought with it many new challenges. In particular, it has become increasingly apparent that while deep neural networks are highly performant, they can also be opaque and brittle. We do not have enough understanding of why and when they work well, and why they may fail completely when faced with new situations not seen in the training data.  

      Our research group has developed a symbolic approach to explaining the behavior and verifying the properties of machine learning models, which is based on sustained advances in logical and probabilistic reasoning. Our approach facilitates the analysis of a neural network, helping us to understand its behavior, and in turn, providing directions towards learning better and more robust models.

    • Real world experience working with (and developing) AI/ML models and tools, for preparation either for research in a graduate program (PhD), or for preparation for research/practice in AI/ML fields in industry.

      1. Statistical modeling and programming
      2. Reading papers

    • Dr. Arthur Choi, achoi13@kennesaw.edu

  • 2021-2022 First-Year Scholars: Donovan McGregor, Computer Science; Cesar Lucena, Computer Science

    • Using brainwave, automated action detection enables us to develop TV control, smart-home climate control, speech detection, speech to activity transformation, etc. This application helps people to make their life better daily. For example, people can command a device/smartphone to execute 'switch off the television. In addition, people who have limited ability to speak or performing specific daily activities can utilize this brainwave signal to automate their needs and presumably helping to do these activities to some extent. 

      In this research project, our goal is to study the feasibility of such applications using brainwaves. We envision setting up an experimental environment where we can disseminate, gather and develop our knowledge base to understand brainwave signals and collect sensor signals. Our research group has investigated, designed, and created machine learning and deep learning algorithms. In this project, we utilize this knowledge, codebases, analyze brainwave signals, and develop a novel application.

      1. Develop scientific literature review skills by reading and analyzing technical articles, blogs
      2. Real-world experience working with sensor devices/signals, AI/ML models and tools for preparation for research in AI/ML in industry
      3. Improve communication and scientific writing skills
      4. Present research results
      1. Reading papers and technical articles
      2. Use sensor devices and collect sensor signals
      3. Execute and modify (if needed) a sample ML/AI source code and generate and analyze results
      4. Meet weekly (in-person or virtually) and report progress

    • Dr. Md Abdullah Al Hafiz Khan, mkhan74@kennesaw.edu

  • 2021-2022 First-Year Scholars: Charlie McLarty, Computer Science;  Leigha Benford, Computer Engineering

    • The landscape in modern computing environments, as edge and Internet-of-things (IoTs) are becoming increasingly popular. This project aims to address the performance and energy issues at the edge platforms using adaptively data-driven learning and control. We focused on studying the resource management and energy consumption on different edge devices, and further explored the opportunities by creating data-driven runtime and tailoring edge framework and data plane. The goal of this project is to develop the possible solutions for highly-efficient and low-power edge computing and infrastructures.

      Specifically, this project will: 1) Understanding the techniques and state-of-the-art edge computing platforms; 2) Analyze the semantic gaps in the cloud and the edge, and design solutions to bridge the gaps; 3) Using the data-driven execution model to provide effective, efficient, and low energy components; 4) increase the fundamental understanding of cloud-edge systems in resource management and energy control.

      1. Attain an ability to design, implement, and evaluate edge computing system, process, component, or program to meet desired needs
      2. Gain system research skills through weekly meeting
      3. Gain knowledge in using IoT devices to run different applications with much
        efficiency and better performance
      4. Work collaboratively with other undergraduate and graduate students
      5. Improve the ability to solving real-world problem by self

    • Weekly group meeting and report the progress:

      1. Share the results on Microsoft teams with advisor in discussion
      2. Design and implement a computer-based system
      3. Evaluate the system performance using microbenchmarks or applications
      4. Present his work on the C-day of CCSE college

    • Dr. Kun Suo, ksuo@kennesaw.edu

  • 2021-2022 First-Year Scholars: Carson Bell, Computer Science; Huan Mai, Computer Science

    • Due to the ubiquitous mobile devices with embedded sensors and connectivity over the internet, many applications are trying to get our consent of sharing personal data. Although there always exist terms and policies for sharing our data, we still don't know what our data is exactly used for. Since a large volume of our daily and sensitive data is used in constituting systems, and we are concerning about the compromise of their personal information, it is necessary to propose innovative secure and private data aggregation method to prevent users' confidential information from illegal revealing while efficiently utilizing massive data generated from users.

      In this project, we are going to explore a cutting-edge privacy preservation method called differential privacy (DP). With DP, we can keep our data confidential while we are involving in a survey or data reporting system. The objectives of this project are to understand security/privacy issues during data sharing and design a mobile application for secure/private data aggregation. In addition, there are trade-offs between the effectiveness of privacy protection and the convenience of data collection, communications, and energy consumption, which need proper considerations in system designs.

      1. Apply fundamental and disciplinary concepts and methods that supports the research project.
      2.  Attain the ability to identify, analyze and solve problems creatively.
      3.  Investigate the cutting-edge security and privacy techniques.
      4.  Experience Graphic User Interface (GUI) design.
      5.  Learn the principles of academic writing and research presentation skills.
      6.  Collaborate with other graduate and undergraduate students with effective oral and written communication.
      1.  Weekly meeting and updates.
      2.  Design the differentially private based data aggregation method.
      3.  Develop the app for secure/private data aggregation including a graphic user interface.
      4.  Prepare presentations for literature review or key findings in the project.
      5.  Final research project reports.

    • Dr. Xinyue Zhang, xzhang48@kennesaw.edu

  • 2021-2022 First-Year Scholars: Merrick McPherson, Computer Science; Avery McDaniel, Biology

    • The project aims to build a web application showing predictions for the risk of developing diabetes or its consequent complications based on Artificial Intelligence (AI). The application will use real-world datasets and show recommendations for self-management of diabetes towards data-driven precision care.

      1. Improve research and technical skills.
      2. Develop literature reviews about artificial intelligence for diabetes care.
      3. Implement web applications using real-world data.
      4. Improve communication skills and scientific writing skills.
      1. Literature review and report of findings regarding artificial intelligence for Diabetes care.
      2. Develop a web application for showing predictions for the risk of developing diabetes or its consequent complications.
      3. Attend weekly meetings and report weekly updates.
    • Dr. Liang Zhao, lzhao10@kennesaw.edu

  • 2021-2022 First-Year Scholar:  Timothy Williams, Computer Science

    • This project will analyze the Dark Web related contents to extract meaningful information as part of cyber security intelligence gathering. The dark web is composed of a vast amount of unstructured and directly inaccessible information.  The project will use Tor-based access to pages followed by identifying individual forums/users, and relating them to accessible web contents. The outcome will be a curated dataset including collection of URLs, set of keywords, actors, for researchers. State of the arts tools related to network, browser and data analysis be used. The project is planned to be disseminated via conference and symposium. No background knowledge of network, html is required as the project will allow the scholar(s) opportunities to learn those topics.

      1. Read and analyze technical articles on dark web, access and analysis of web pages
      2. Analyze html pages to extract critical information, identify dark web forums to link actors
      3. Develop a dataset related to dark web information crawling
      4.  Present the research results to local and regional audience
      1.  Analyze articles related to dark web, crawl dark web space using appropriate tools such as Tor
      2. Gather webpages and analyze html pages, identify critical information of interests
      3. Develop data representation for analyzed pages
      4. Attend weekly meeting with project supervisor in person or virtually
      5. Write report based on research effort

    • Dr. Hossain Shahriar, hshahria@kennesaw.edu

  • 2021-2022 First-Year Scholars: Brendon Antoine, Information Technology; Anh Duong, Computer Science; Meti Haile, Computer Science

    • The project aims to develop a web application that helps to show the records of management levels that come from an IoT device. The application will get the information from a real-time database and then show results for future diabetes management recommendations.

      1. Improve research and technical skills
      2. Develop literature reviews about diabetes management IoT applications.
      3. Implement web applications for real-time database
      4. Improve communication skills and scientific writing
      1. Literature review and report of findings regarding diabetes management with IoT
      2. Develop a web application for showing real-time information about blood sugar levels
      3. Attend weekly Dr. Valero's group meetings (Fridays 10:30 - 11:30 am). Virtually
      4. Report weekly updates

    • Dr. Maria Valero de Clemente, mvalero2@kennesaw.edu

  • 2021-2022 First-Year Scholars: Salar Kashif, Information Technology;  Abel Yared, Undeclared; Amyr Murray, Computer Science; Jordan Bonar, Computer Science

    • With the current pandemic (COVID-19), healthcare professionals and industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided systems for screening, tracking, predicting the spread of COVID-19, and finding a cure against it.  

      In this project, which is designed for undergrad students, we are planning to explore the role of machine learning techniques and in particular federated machine learning in the diagnosis of COVID-19 patients and how using federated machine learning can improve both the privacy and accuracy of COVID-19 diagnosis. 

      Project Phases:
      Phase 1: Literature review, Project Plan and Design
      Phase 2: Data Collection and preparation
      Phase 3: Implementation
      Phase 4: Experiment and Evaluation

    • By the end of this project, a student should be able to:

      1. Explain the major steps of an end-to-end data analytics project.
      2. Select proper data analytics models for a given problem.
      3. Develop and execute a data analytics solution in a decentralized environment.

    • A weekly meeting with the faculty advisor in order to follow the aforementioned phases is necessary. In addition, the student is expected to provide a brief report and presentation in each session.

    • Dr. Seyedamin Pouriyeh, spouriye@kennesaw.edu

  • 2021-2022 First-Year Scholars: Joshua Whorton, Computer Game Design and Development;  Margarita Marquez, Computer Engineering;  Victoria Davis, Computer Science;  Stephen Pangilinan, Computer Game Design and Development; Gabriel Craven, Computer Science

    • During the last decade, there have been two significant developments in the games space: 1) the scope and scale of large video game development projects have increased dramatically and 2) there has been a rapid rise in independent game development studios pursuing novel and creative game designs. Unfortunately, this greater scope and scale also creates an increased need for game content, which increases the need for human resources from artists and game designers. A result of which is the cost of video game production has drastically increased, making even the largest of game development studios risk-averse.

      At the same time, greater access to professional game development software has given independent game developers a broader set of tools, leading to an explosion of novel and creative games that push the boundaries of game design. However, independent developers are often very lean organizations working with minimal budgets and often constrained in what they can accomplish by a simple lack of production capability. An opportunity exists to support both the development of the medium as well as game designer professionals through automating parts of the game content generation process using artificial intelligence.

      The purpose of the Minimalist Generative Game Narratives project is to identify new ways of using computers to generate content for games, with the end goals of reducing the financial risks associated with large game production, as well as providing tools and understanding that allows smaller studios to tackle bigger problems. With my students, we will explore how games can be designed with minimal narratives that still maintain believability and enjoyment for players, as well as generate understanding for how this knowledge can be applied to game narratives generated using artificial intelligence.

      Thanks to the same tax credits that benefit the film industry in Georgia, there is a thriving economy of local independent game studios. The activities in this proposal allow KSU students and graduates to have significant impact on this economy by supporting the growth of the local games industry, which will help attract larger game studios to the area thereby providing further local career opportunities for KSU graduates.

    • In participating in this program, students will learn to:

      1. Apply in-depth understanding of game studies and game design studies to game design
      2. Accurately design, implement, and test game prototypes
      3. Effectively formulate, design, and execute user-centered studies of games and game design
      4. Effectively perform data analysis of qualitative and quantitative data
      5. Work collaboratively as part of a software engineering team

      In addition to knowledge outcomes, students will also finish the year with a portfolio of implemented game prototypes and a research portfolio. These products will increase student ability to be competitive as they pursue employment in the games industry, and inspire confidence in student research so that students can pursue their own research interests and initiatives at both undergraduate and graduate level.  

    • Weekly duties will consist of exploring different ways of designing narrative experiences in games. This involves:

      1. Performing scientifically grounded game design
      2. Implementing prototypes using tools such as Tracery, Minecraft, or Unity 3D
      3. Testing and evaluating the user experience prototypes
      4. Summarizing and reporting findings from studies
      5. Weekly meetings and reports

    • Dr. Henrik Warpefelt, hwarpefe@kennesaw.edu

 








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