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College of Computing and Software Engineering 2022-2023 Projects

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

  • 2022-2023 First Year Scholars: Bryce Wishart, Computer Science  Kyle Hillhouse, Computer Science  Yuliana Pacheco, Computer Science

    • Artificial intelligence improves automated decision-making performance in real-world applications. However, incorporating humans in the application loop achieves decision superiority in various environments, including a high-risk, time-critical nature. On the other hand, brain-to-brain synchrony provides an understanding of social collaboration, team activities, mutual understanding, etc., among multiple people simultaneously. Examples of brainwave-based brain-to-brain synchrony are human-AI teaming, including parent-child, friend-to-friend, student-teacher synchrony, etc. Human AI teaming enhance this synchrony to make decision effective and sustainable. For example, the virtual tutor can be synchronized with students to answer their concerns. In this application, we explore brainwave signals to explore the synchrony between two entities (e.g., student-teacher) by analyzing sensor signals and developing machine learning algorithms to make the decision smarter secure, and trustworthy.

      In this research project, we aim to study the feasibility of such applications using brainwave sensors by improving the knowledge base from scratch (without expert knowledge). Furthermore, we envision setting up an experimental environment based on our acquired knowledge to develop a prototype by collecting sensor signals and analyzing and developing machine learning, deep learning (AI) algorithm. Our research group has investigated, designed, and created machine learning algorithms. In this project, we utilize this knowledge, and codebases, analyze brainwave signals and develop a novel application. 

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

    • Student will work in this project in hybrid or online setting depending on the students understanding and need. Time to time student performance will be evaluated and changes will be accommodated accordingly.

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

  • 2022-2023 First Year Scholars: Gabe Livengood, Computer Science  Joshua Jones, Computer Engineering  Meghana Gotety, Computer Science

    • In early 2022, Oak Ridge National Laboratory, HPE, and AMD officially launched the world's first exascale supercomputer, Frontier. Supercomputers play a vital role in developing scientific research, national defense, security, etc. To maintain United States' leadership in supercomputing society, currently, we must start exploring the next-generation system architectures for zettascale supercomputers. 

      Considering various limitations, the Department of Energy (DOE) recommended that the power consumption of an exascale supercomputer should be less than 20 MW, and the current estimated power of the Frontier is 21 MW. However, since 20 MW is close to the power consumption limit that can be supplied to supercomputers by existing technologies, the DOE may not significantly increase the power budget for zettascale supercomputers. Therefore, designing power-efficient whole-system architectures is crucial for the success of next-generation zettascale supercomputers.

      This project will explore low-power supercomputer architectures based on the current exascale design. For example, one possible solution is the extreme heterogeneous architecture, which includes a mix of CPUs, GPUs, and application-specific accelerators (E.g., Machine Learning accelerators). To evaluate our designs, we will modify available code modules and build the system using the Structural Simulation Toolkit (SST; An open source computer system simulator; C++). All the coding tasks and benchmark executions will be completed on the KSU High-Performance Computing cluster.

      • Explore the state-of-art supercomputer architectures.
      • Learn and improve C++/Python coding skills, and learn how to execute tasks on a High-Performance Computing cluster
      • Learn how to use, manage and improve a large-scale open source software
      • Obtain writing and presentation skills
      • Learn how to identify the bottlenecks of the current supercomputer and proposal possible solutions
      • Write a technical report
      • Meet advisor in-person or online weekly to report the research progress
      • Read papers and modify the open source simulator
    • Hybrid
    • Dr. Bobin Deng, bdeng2@kennesaw.edu

  • 2022-2023 First Year Scholars: Fatima Salman, Compuer Science  Mahimna Patel, Computer Science  Ricardo Vazquez, Computer Science

    • Research project is titled “Low-Power Low-Cost Long Range Radio Networking for Internet-of-Energy (IoE) Manufacturing Industry”. This project aims to explore new techniques for the design of efficient algorithms on energy-starved IoE manufacturing networking systems, and a new cloud network paradigm such as a consolidation of Low-Power Wide-Area Network (LPWAN) such as using Long Range Radio (LoRa) technology with edge cloud computing to support QoS both energy-starved IoE and general Internet-of-Thinks (IoT) data communications. LoRa is a low energy consumption, low bit rate, cost-effective, and license-free IoT, which has a significant long-range. LoRa can be combined with software-defined networking to create unified, adaptable, rapid-deployable networks. Therefore, these project solutions are applicable to many high-impact situations where communication is lost, as well as monitoring energy intake and usage patterns at the data-driven manufacturer site, zone, system and device level.

    • At the end of the project, students should be able to:

      • Conduct high-impact research to solve crucial computer network problems
      • Analyze computer science and engineering approaches, be able to integrate software, hardware, communication systems, and network infrastructures
      • Understand how to read academic papers in Computer Science
      • Prepare and deliver a presentation demonstrating understanding of a paper or new tool or piece of technology
      • Analyze results and shortcomings of published work
      • Develop a research project and paper

    • Students will do research related to a research topic of computer networks, especially in Low-Power Low-Cost Long Range Radio Networking with Internet-of-Energy technologies. Thus, I advise students to produce at least one manuscript submission targeting IEEE conferences or Journals per semester. 

    • Hybrid

    • Dr.Ahyoung Lee, alee146@kennesaw.edu

  • 2022-2023 First Year Scholars: Matthew Uliasz, Computer Science  Noah Dillard, Computer Science  Parker Arneson, Computer Science  Wyatt Bonno, Computer Science

    • The blockchain is an open standard, de-centralized, incorruptible, self-executing, distributed database public digital ledger that can store not only economic transactions but effectively any type of record. Ethereum is a decentralized, open source blockchain with smart contract functionality. Ethereum’s use cases are vast and expanding fast, offering blockchain projects enhanced efficiency, security, and decentralized equity to industries across the globe. This project will begin with learning of Blockchain and Ethereum, then followed by the development of a system with Ethereum.

      Possible Systems (with smart contract features) to Develop:

      Option 1: A Cryptocurrency system

      Option 2: A Healthcare system

      Option 3: A Decentralized Finance system

      Option 4: A Polling system

      Option 5: A Supply Chain system

      Preferred prerequisite knowledge:
      Programming experience (Python, Java, or others)

      Skills/Knowledge you will learn:
      Ethereum, EVM, DApps, Web3, Solidity, Remix, smart contracts, Truffle, JSON, etc

    • Major Work, Milestones and Expected Outcome

      Stage 1. Basic understanding of Blockchain and Ethereum 

      Stage 2. Source code for a system developed with Ethereum. And a google site to host the modules in your system. Suggested modules (students can revise) are:

      • Create transactions
      • Create accounts
      • Send transactions between accounts
      • Create smart contracts in Remix/Solidity and deploy them to a blockchain
      • Switch between different Blockchains
      • Deposit, withdraw using smart contracts
      • Lifecycle of smart contracts
      • Exception Handling: Require, Assert and Revert in Solidity
      • Solidity Inheritance examples
      • Solidity Events and Return Values

      References (your system should be your own work and different from others)

      • Ethereum Tutorial (https://www.tutorialspoint.com/ethereum/index.htm)
      • A CryptoFlight project that contains 4 labs (https://andyafk.gitbook.io/blockchain/)
      • Blockchain and Smart Contracts (https://github.com/cryptobuks/educational-Blockchain-Stuff-Smart-contracts)
      • How To Build A Blockchain App with Ethereum, Web3.js & Solidity Smart Contracts (https://www.dappuniversity.com/articles/how-to-build-a-blockchain-app)
      • Building a “Hello World” smart contract on Ethereum (https://medium.com/shokone/https-medium-com-shokone-building-a-hello-world-smart-contract-on-ethereum-f303c7d05f0)
      • Ethereum Smart Contracts in Python: a comprehensive(ish) guide (https://hackernoon.com/ethereum-smart-contracts-in-python-a-comprehensive-ish-guide-771b03990988)

    • The students will perform:

      Task 1: Conduct research survey on various computing environments for our approach

      Task 2: Gather real world data sets to test our approach

      Task 3: Implement our algorithm and conduct experiments 

      Task 4: Collaborate with the advisor on a research paper

      Task 5: Help the advisor prepare for grant proposals for NSF programs

      The research work in this project will be submitted to several prestigious conferences such as IEEE INFOCOM: IEEE International Conference on Computer Communications, and the research work will be further extended for journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence. The research work in this project will also be integrated to grant submission to NSF grant proposals for Research Experiences for Undergraduates (REU) (19-582), Improving Undergraduate STEM Education: Education and Human Resources (IUSE: EHR) (21-579), etc.

    • Hybrid

    • Dr. Yong Shi, yshi5@kennesaw.edu

  • 2022-2023  First Year Scholars: Arshia Charkhian, Computer Science  Austin Frazier, Computer Science

    • The Internet of Things (IoT) is described as networks of small physical devices, embedded with sensors, software, and other technologies, that easily exchange data with other devices and systems over the Internet. The convergence of traditional technologies from wireless networking, control systems, and automation with miniaturization and low-powered devices contributed to the development of IoT, spurred on by strong demand and rapid growth in smart home automation and smart cities. Affordable interoperable IoT systems are increasingly ubiquitous in daily life. These IoT devices, working closely together, orchestrate a range of tasks, increasingly used for such activities as programmable personalized control of heating, cooling, and security in homes and offices. As these IoT devices become more capable, more computationally demanding tasks can be performed by these devices singly or in combination as a local distributed network bringing computing closer to the location where needed to improve responsiveness, i.e., at the edges of the Internet. The challenge is to ensure the highly capable, timely performance, seamless collective operation of IoT devices with edge computing and even cloud services as an efficient purposeful system.

      This project studies the relationships between system resource utilization and energy efficiency in various edge and IoT systems in order to better understand how to optimize the key performance parameters of edge computing systems. This project explores mitigating the inefficiency in edge systems through a data-driven approach.

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

    • Weekly group meeting and report the progress:

      • Share the results on Microsoft teams with advisor in discussion
      • Design and implement a computer-based system
      • Evaluate the system performance using microbenchmarks or applications
      • Present his work on the C-day of CCSE college
    • Hybrid
    • Dr. Kun Suo, ksuo@kennesaw.edu

  • 2022-2023 First Year Scholar: Bao Tran Ho, Computer Science

    • A two-sided eCommerce market, where consumers and suppliers can exchange products and services on an online platform, is a revolutionary marketplace business model. It gains popularity in both buyers and sellers due to the simplification of trading process. Facebook Shops, an eCommerce market for small business, saw 1 million monthly active users in March 2021. In such large-scale applications, the recommender system plays a crucial role to provide the relevant suppliers for the consumers, as well as locating the targeted consumers for the suppliers. Although the recommender systems enable a convenient way to discover interests, the traditional ranking algorithm faces a fairness problem, where the under-ranked suppliers only obtain limited exposure on the market. In this case, a large set of consumers would be principally directed to a small portion of suppliers with a higher ranking. Consequently, the exposure and visibility of these beneficial suppliers will in turn influence the ranking results of a recommender system, which promotes the rich-get-richer problem. The major unfairness comes from the traditional recommender system is that it aims to maximize the consumers' satisfaction, where the tail-end suppliers find incapable with limited exposure. Therefore, we propose a novel many-to-many matching method to fairly allocate the exposure and visibility of each supplier. Eventually it will reach to a balanced satisfaction between consumers and suppliers, attracting more users to the two-side marketplace. There are plenty of existing work discussing about the fairness and bias in the marketplace recommendation systems or ranking models. However, all prior work proposed to solve the recommendation ranking problem in a centralized manner. In our proposed matching algorithm, the problem can be solved in a decentralized manner.

      Research Tasks:

      (1) We first give the definition of fairness in the two-sided eCommerce markets by considering the relevance of a supplier for a given consumer. 

      (2) We leverage a many-to-many matching algorithm to achieve stable pairing results between consumers and suppliers with the consideration of both relevance and fairness, where no one is able to unilaterally deviate from current pairing with higher utilities.

      • Apply fundamental and disciplinary concepts and methods that supports the research project
      • Attain the ability to identify, analyze and solve problems creatively
      • Investigate the cutting-edge decentralized algorithms
      • Propose solutions to fairness optimization problems
      • Learn the principles of academic writing and research presentation skills
      • Collaborate with other graduate and undergraduate students with effective oral and written communication
      • Weekly meeting and updates.
      • Design the decentralized matching algorithms for two-sided eCommerce markets with the consideration of fairness.
      • Develop the system for fair matching between suppliers and consumers.
      • Prepare presentations for literature review or key findings in the project.
      • Final research project reports and poster presentation in annual symposium
    • Online

    • Dr. Xinyue Zhang, xzhang48@kennesaw.edu

  • 2022-2023 First Year Scholars: Asia Shavers, Computer Science  Ava Norouzinia, Computer Science  Corey Brookins, Computer Science  Kenneth Burke, Computer Science  Marie Nassif, Computer Engineering  Miranda Dominguez, Computer Science  Shamar Lake, Computer Engineering

    • Ransomware is a type of malware that uses encryption to hold a victim's personal data hostage for a random. If a user or organization is held victim to ransomware, their important data is encrypted so that it is inaccessible to them. This data can include files, databases, or applications. Ransomware can be introduced via untrusted third-party software, engendering Software Supply Chain Security crucial in the space of cybersecurity research. Software Supply Chain Security investigates the entire process of how software gets compromised at different stages from development to deployment. This project aims to explore various ransomware and software supply chain attack scenarios and identify best practices to prevent them. This project is designed as a collaborative group project and seeks 4-5 qualified students motivated to research in a group environment. No particular background knowledge on ransomware, supply chain security, or coding is required. The project to be led by Drs. Shahriar (ransomware aspect) and Sakib (software supply chain security aspect).

    • This project will well-verse participant students in:

      • Understanding multifarious ransomware attack scenarios, the dynamics of the software supply chain, software supply chain attacks, and the dire need for software supply chain security
      • Explaining the state-of-the-art tools and techniques to prevent attacks from ransomware and software supply chain security issues
      • Assisting the supervisors in preparing and publishing a comprehensive literature review on state-of-the-art tools and techniques in the conference/journal
      • Attend weekly/bi-weekly meetings with the project mentors as scheduled, and join lab meetings as schedule permits
      • Discuss and communicate findings from reading various sources
      • Participate in hands-on tutorial activities to have a better understanding of the concepts
      • Assist the mentors in writing a scholarly paper
    • Hybrid

    • Dr. Hossain Shahriar, hshahria@kennesaw.edu

  • 2022-2023 First Year Scholars: Aryan Patel, Computer Science  Benjamin Sleszynski, Software Engineering

    • There has been an increasing effort in cybersecurity teacher preparation for secondary education in recent years. However, one of the significant challenges teachers face when trying to teach cybersecurity topics is the lack of expertise, time, and resources needed to develop a cybersecurity curriculum for their classrooms. With this need in mind, the goal of this project is to develop a standards-based cybersecurity curriculum for secondary education to help increase student cybersecurity literacy and build a robust pipeline of future cybersecurity talents. The expected outcome of this project is to provide a comprehensive, ready-to-use, standards-based, hands-on cybersecurity curriculum that is readily available for secondary education teachers to implement in their classrooms. This project will also develop a website that shows the research contents, research team publications, and researchers’ names. 

      • Design the cybersecurity activities with the mentors
      • Perform mission planning
      • Learn statistical analysis
      • Research and develop learning activities and lesson plans
      • Draft scholarly papers
      • Setup lab environment using Raspberry Pi
      • Web development

    • Students will meet mentors every week for the progress report and submit the weekly report to the mentors. Each week, students will perform the duty of research and complete the tasks. Every month, students will attend a research seminar to present their research and discuss it with other students. Students are also encouraged to attend the faculty research seminar to learn the emerging topics and technologies that related to their research. 

    • Hybrid
    • Dr. Shirley Tian, shirley.tian@kennesaw.edu

  • 2022-2023 First Year Scholars: Abhimanyu Malik, Information Systems  Gary Xue, Computer Science

    • The ongoing outbreak of COVID-19 has been a serious threat to human health worldwide. Scientific research in COVID-19 related area has been considered important and urgent. As the technology develops, computational studies and applications in scientific research has been super useful and helpful, as an addition or initial prediction tool to the actual biological and clinical experiments and studies. This data science and computational algorithms in COVID-19 project will be based on fundamental data science strategies, including data collections, data cleaning, data analysis, data visualization, etc. By participating this project, students will be able to explore the data science application in the real world, especially in health-related area. Students will learn the basic techniques and methods that will be applied in not only this project but also in their future projects. 

    • This data science and computational algorithms in COVID-19 project will be based on fundamental data science strategies, including data collections, data cleaning, data analysis, data visualization, etc. By participating this project, students will be able to explore the data science application in the real world, especially in health-related area. Students will learn the basic techniques and methods that will be applied in not only this project but also in their future projects.

    • Students will have at least one meeting or conversation with me every week, so that they will have a better understanding of the project and a good track of weekly outcomes.

    • Hybrid
    • Dr. Yixin Xie, yxie11@kennesaw.edu

  • 2022-2023 First Year Scholars: Ishitha Vallurupalli, Computer Science  Khoa Nguyen, Computer Science  Niklas Knipschild, Information Technology

    • Computer network system administrators need to inspect and analyze network traffic to detect malicious communications, monitor system performance, and provide operational services. However, identifying threats contained within encrypted network traffic, which has become increasingly prevalent, poses a unique set of challenges. Thus, it is imperative to monitor this traffic for threats and malware but do so in a way that maintains privacy. This project aims to develop a machine learning based system that can accurately detect malware communication in this setting.

      • Improve research and technical skills
      • Develop literature reviews about machine learning for cybersecurity
      • Gain understanding and knowledge about computer networking
      • Implement web applications using real-world data
      • Improve communication skills and scientific writing skills
      • Read papers to gain knowledge about computer networking and cybersecurity
      • Literature review and report of findings regarding machine learning for cybersecurity
      • Develop a web application for the detection and analysis of encrypted malicious network traffic
      • Attend weekly meetings and report weekly updates
    • Hybrid

    Dr. Liang Zhao, lzhao10@kennesaw.edu

  • 2022-2023 First Year Scholars: Anaiya Tucker, Computer Science  Emily Espinoza, Interactive Design  Jessica Sunsanto, Computer Science  Nicholas Goolsby, Computer Science  Tyler Kelly, Art

    • 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 this 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:

      • Apply in-depth understanding of game studies and game design studies to game design
      • Accurately design, implement, and test game prototypes
      • Effectively formulate, design, and execute user-centered studies of games and game design
      • Effectively perform data analysis of qualitative and quantitative data
      • 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 new design approaches to games. This involves:

      • Performing scientifically grounded game design
      • Implementing prototypes using tools such as Unity
      • Designing, building, and testing game controllers
      • Testing and evaluating the user experience prototypes
      • Summarizing and reporting findings from studies
      • Weekly meetings and reports
    • Hybrid
    • Dr. Henrik Warpefelt, hwarpefe@kennesaw.edu










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