Thesis/Project Final Defense Schedule

Join us as the School of STEM master’s degree candidates present their culminating thesis and project work. Check back mid quarter for any new quarter defenses.

View previous quarter schedules

Select a master’s program to navigate to candidates:

Master of Science in Computer Science & Software Engineering


Monday, July 24


Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Sreja Babu’s online defense
Project: Gardening Mobile Application Powered by Machine Learning and Artificial Intelligence Technologies

There are many research studies that show that gardening is a helpful hobby to improve physical and mental health in human beings. However, it is a well-known fact that gardening itself requires a lot of time, effort, and knowledge to be successful. While most of the information needed to be good at gardening is spread across various plant resources online, it needs a lot of effort from the gardeners to gather all in one place for the plants of their interest.

In this study, we researched different models and features to identify a combination of a model and features that provide higher accuracy in plant species identification, thereby offloading some of the human hard work to automation and technology, resulting in green gardens and happy people. We built a plant database cross-platform application based on image identification research and Generative Pretrained Transformer (GPT). The application allows users to identify plant species using digital image processing and machine learning techniques and to automatically add the plants into a “Virtual Garden.” For the plant recognition aspect, we are able to identify the plant species with an accuracy of 91.6%, using the Support Vector Machine (SVM) model. The SVM model plugs directly into our backend REST server. The key advantage of our backend implementation is the possibility to swap our current classifier model with an improved model at any time, therefore, keeping the option to improve our accuracy with support to more plants. Currently, our application is capable of identifying 32 different plant species.

Once the user adds plants to the Virtual Garden, our application will be able to fetch detailed information about each plant from the plant database that we built. This information comes from our automatically populated plant database built on top of Generative Pre-trained Transformer, Large Language Models (LLM). The GPT-3.5-Turbo LLM has been integrated with our backend application. We have a continuous process (DB populator) that updates our internal plant database continuously. The DB populator, research on models and features, and the questionnaire prompt set are the key contributions in our research. The DB populator uniquely enables our plant database to scale for new plants and species when more data becomes available later. We leverage the fine granular pruned gardening information with the help of GPTs to notify users when to water or replenish the soil and other useful information. These notifications will offload a lot of planning that a gardener typically has to perform and convert them to a set of simple instructions to follow easily.

The uniqueness of our application when compared to other applications is that it leverages the GPT LLM models to automate the process of information gathering as opposed to relying heavily on user contributions, and plant experts like the existing applications. Based on our research, our application uses an SVM model with a unique set of features that has a higher accuracy than the rest of the other feature sets used. The results from the usability study proved that this application reduced a significant amount of work and time taken to gather all the information. It also provided crisp information as opposed to the information gathered manually. For future work, we intend to add more social features and also measure the impact of the longevity of sticking with gardening with the app as compared to without using it. Overall, this application serves as a very useful tool that facilitates an enjoyable gardening experience for gardeners.

Thursday, July 27


Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Discovery Hall Room 464
Project: Quickcheck Application for Mobile Platforms: Architectural and Release Planning

According to the Visual Health Initiative (VHI) of the Centers for Disease Control (CDC), 6.8% of children under the age of 18 have been diagnosed with visual impairment. It is estimated that 60% of children with learning disability actually have undiagnosed vision problems and 80% of learning is visual. It is crucial to identify and diagnose any potential vision impairments and eye illnesses as early as possible since they cannot self-report their vision impairment. When children struggle with visual tasks, they may not be able associate their difficulty with an issue with their eyes. Adults generally exacerbate the issue by misconstruing the symptom as a learning disability. A quick examination termed a vision screening, commonly known as an eye test, searches for suspected vision issues and eye conditions.

QuickCheck is a mobile application that enables school nurses to diagnose students for suspected vision health concerns. It is not intended to replace an eye exam, but rather to provide the students with a “quick” check-up so that potential patients can be referred to professionals, so that children who show signs of vision issues can have the necessary eye exams.

The application underwent comprehensive testing using specialized tools, including UI automation tools and load testing tools such as Apache JMeter. This rigorous testing process facilitated an in-depth evaluation of the app’s performance, user interface, and responsiveness, ensuring its reliability and efficiency in various usage scenarios. Based on a series of rigorous test runs using these criteria, it was determined that the application has reached a state of readiness suitable for initial launch so that we may proceed with clinical testing.

Friday, July 28


Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Mary Eyvazi’s online defense
Project: Efficient Receipt Understanding Using Model Compression Techniques For a Cost-Sharing Application

In today’s fast-paced and interconnected business world, managing expenses and processing receipts efficiently is a pressing concern, not only for organizations but also for individuals navigating personal and shared expense systems. The traditional manual approach to receipt processing is laborious and error-prone, resulting in inefficiencies and potential financial discrepancies that can adversely affect both individuals and businesses alike.

This project seeks to address these challenges by focusing on the development of a cost-effective solution to automate receipt processing, with a specific emphasis on cost-sharing systems. We also shed light on the complexities associated with managing shared expenses and identify the limitations of existing deep learning models, particularly concerning their high computational and storage demands.

To overcome these obstacles, we propose the utilization of two model compression techniques: knowledge distillation and model quantization. By leveraging these cutting-edge methods, we aim to significantly reduce the size and computational requirements of visual document models utilized in receipt processing. Through rigorous evaluation, we assess the effectiveness of these techniques, ensuring that the resulting solution maintains exceptional accuracy and performance standards.

To demonstrate the practicality and efficiency of our proposed solution, we develop a cost-sharing application that showcases its seamless integration into real-world scenarios. Our ultimate objective is to democratize receipt processing by providing a more accessible and affordable solution for end-users, empowering both individuals and organizations to streamline expense management processes and make informed financial decisions with confidence. With this project, we aspire to foster greater financial transparency and alleviate the burden of manual receipt processing, thus enabling individuals and businesses to thrive in today’s dynamic economic landscape.

Thursday, August 3


Chair: Dr. Geethapriya Thamilarasu
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Christian Rolph’s online defense
Project: Semaphore: Mobile Application for the Hearing-Impaired Using Peer-To-Peer Connections in an Ad Hoc Network

Few mobile applications exist for the deaf and hearing impaired to be able to communicate, and those that do exist typically rely on the Internet to be able to function. This creates a problem for the deaf community when they want to use their app in a location that has poor or no Internet service. This capstone project aims to develop a mobile application for the deaf that can be used without the Internet. The proposed solution uses Bluetooth Low Energy (BLE) for the underlying network protocol to allow direct peer-to-peer message passing. The CoreBluetoth framework, provided by Apple, serves as the primary interface between the application and BLE functionality. The project builds on this protocol to create an ad hoc mesh network, allowing peers that are not directly within Bluetooth range of one another to communicate. The implementation uses the iOS operating system and a mobile platform to be easily usable for most users using their smartphone. It allows for real-time translation of speech to text, and two-way communication between a network of connected users.

The application was tested in several key areas including transcription accuracy, scalability, usability, and resource efficiency. Transcription testing primarily focused on ensuring that the speech-to-text functionality of the application was of a high enough quality to support everyday conversation. The application takes a heavy dependence on the voice-to-text APIs provided by the iOS operating system that operate on-device, which generally performed very well. Scalability testing focused on how well the application could handle multiple users in a single chatroom, and how many chatrooms could be created simultaneously without interfering with one another. Usability testing was conducting using a beta test with real users and asking them to evaluate their experience on a feedback form. Finally, resource efficiency testing focused on evaluating the application’s impact on battery life compared to that of other popular apps.

Overall, this project met its goal to provide a usable offline communication mechanism for the deaf community. It demonstrates that BLE is a reasonable choice as an underlying network protocol for this purpose. This project’s ad hoc network demonstrates potential for applications in other areas including disaster relief, military applications, and Internet of Things devices. Future research and work can build on this project to expand the use of Bluetooth to create such networks.

Monday, August 7


Chair: Dr. Yang Peng
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Zuodong Wang’s online defense
Project: Multiple Vehicle Task Scheduling for Vehicle Based On-Demand Mobile Edge Server

The rapid growth of mobile devices and the increasing demand for real-time data processing have led to the emergence of mobile edge computing (MEC) as a promising solution to address the limitations of traditional cloud computing. MEC leverages the proximity of edge servers to mobile users to provide low-latency and high-bandwidth services. In this context, the efficient dispatch and scheduling of vehicle-based, on-demand mobile edge servers (VOMES) have gained significant attention. This report proposes a vehicle movement and task allocation approach for VOMES.

The objective is to maximize the total operating profit while considering the operational costs and mobility constraints of the VOMES. To achieve this, we develop a mixed-integer linear programming (MILP) formulation that considers various parameters, including the computational capacity of the VOMES, the processing requirements of the tasks, the vehicle mobility patterns, and the operational costs. By formulating the problem as an MILP, we enable the use of optimization techniques to find the optimal task allocation and scheduling solution. To handle the dynamic nature of the VOMES environment, we propose two approaches. In the first approach, an initial schedule is generated based on the current knowledge of the tasks and the VOMES locations. In the second approach, the schedule is updated periodically to adapt to the changes in task arrivals and VOMES availability. To facilitate dynamic scheduling, we employ a heuristic algorithm that considers the task needed capacity, VOMES mobility patterns, and the proximity of the VOMES to the task locations.

The proposed approach has been evaluated through extensive simulations using realistic mobility and operation constraints. The results demonstrate that the proposed approach achieves significant improvements in terms of operation profit compared to baseline scheduling strategies.

Wednesday, August 9


Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Rami Abed’s online defense
Project: Skurupuru – A Secure, Mobile-First Schoolpooling App

Traffic congestion is wasteful of time and emissions. Around schools, it creates hazards to students and staff and it clogs arterials for other commuters during drop-off and pick-up times. In response to this problem, the city of Bellevue developed a TDM program called SchoolPool. However, Bellevue’s Districtwide Travel Survey reveals that the same proportion of people carpooled in 2022 as did in 2017 – 11%. This despite 42% of parents expressing interest in carpooling in another 2017 survey. While considering various approaches to increasing carpooling over the years, Bellevue schools still lack a viable technical solution to address the problem.

We develop「スクールプール」- Skurupuru – a secure, mobile-first, featureful, and brandable cross-platform app built on a Firebase backend. Skurupuru primarily aims to facilitate carpooling to and from schools. Skurupuru is designed in response to requirements elicited from city of Bellevue staff, incorporates stakeholder input from school staff and parents, addresses limits in previous technical solutions, and is mindful of findings in carpooling and schoolpooling studies.

Friday, August 11


Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Remya Mavalia Kizhakkeveettil’s online defense
Project: Icare – A Virtual Assistant for Mental Health Powered by AI

This presentation describes the design and implementation of a virtual assistant to support mental health, using artificial intelligence and machine learning. Currently, mental health related issues are increasing among individuals because of various reasons. Sharing their feelings with someone who cares about them plays a major role in resolving these issues. Virtual assistants that can simulate human conversations using artificial intelligence can be very effectively used to communicate with individuals facing challenges. iCare is an application integrated with a virtual assistant or chatbot intended to provide support for individuals suffering from mental-health-related issues. The iCare virtual assistant provides a safe, private, virtual environment for users to share their feelings, and get empathetic response that improves their mental condition. The virtual assistant relies on machine learning algorithms to formulate the response for users. The bot understands the user’s query and triggers an accurate response as text or speech with the help of natural language processing. iCare implements a different approach from current solutions, by using a combination of multiple techniques to provide accurate responses to its users. The project is designed to provide support for a range of users like those who are suffering from anxiety, depression, individuals who are unhappy and need some help to improve their present feeling.


Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; Join Harleen Kaur Bhamrah’s online defense
Project: Data Modeler: UWB Web Based Learning Tool for Database Modeling

Object-Role Modeling (ORM) serves as a robust technique for teaching and implementing database and object-oriented design. Its visual representation of real-world entities and emphasis on semantics make it a valuable resource for students and professionals seeking to grasp database design concepts swiftly. Similarly, Logical Data Modeling (LDM) is widely embraced for its ease of learning and adaptability to changes, as it is supported by numerous modeling tools and frameworks. However, many available modeling tools lack comprehensive support for ORM, and Microsoft VisioModeler is incompatible with new operating systems. To address this limitation, the project’s focus is on developing a web-based application that supports ORM, LDM, and SQL conversion and generation, following software engineering principles, enhancing features, and conducting comparative analysis. The main goal is to implement essential features for building ORM models in the initial phase of the database modeling process, while also diligently examining the system to address bugs and non-functional aspects effectively. Additionally, we will prioritize clean code practices, Test-Driven Development (TDD), logging, exploring and implementing exception handling enhancements. The project also emphasizes the learnings and decisions made throughout the tasks.


Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.; Join Apurva Sharma’s online defense
Project: Data Modelling Tool: A Tool to Create Database Models and their Automatic Conversion

The tool primarily focuses on ORM (Object Role Modeling) and LDM (Logical Data Model) techniques. It offers students a practical and visual approach to explore various modeling concepts, create ORM models, convert them to LDM representations, and generate SQL scripts. The tool bridges the gap between theoretical concepts and real-world applications, providing students with hands-on experience and a deeper understanding of database designing principles. Developed as an initiative by Professor Mark Kochanski, the “”Data Modeling Tool”” offers an interactive and visual learning environment for students, facilitating their understanding, application, and exploration of various modeling techniques. Project is inspired by VisioModeler 3.1 which supported ORM modeling comprehensively but is no longer available to use.

This paper presents the development of features and functionalities of the tool with implementation of new architectural design to enhance the forward and reverse engineering of ORM, LDM and SQL models.

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Master of Science in Cybersecurity Engineering

Summer 2023

Thursday, July 27


Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering
1:15 P.M.; Join Seth Don-Hao Pham’s online defense
Project: Evaluating Player Engagement in a Choose-Your-Own-Adventure Game Illustrating Personal Cybersecurity Awareness

The professional environment has seen great success in adapting serious games to raise cybersecurity awareness and skills in the workplace. These games provide scenario-driven experiences allowing players to explore and interact with cybersecurity skills without real-world consequences. Enterprise training requirements ensure employees engage with the games, a function not present for personal cybersecurity, leading the average person to not engage with this type of game in their free time. With an organically engaging game, the integration of cybersecurity scenarios can be introduced in an inviting context to the casual player, leading to higher engagement in cybersecurity awareness. This project evaluates the effectiveness of a cybersecurity game designed to entertain and engage players while increasing their cybersecurity awareness. Based on the feedback from an initial test group, three core concepts were critical for player engagement and enjoyment: an easy-to-handle UI; a fun, exciting story; and player text-length preference. The survey evaluated player preferences and the effect of their evaluation on the game. In addition, participants evaluated the game’s effectiveness based on the framework used in the previous study.


Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering
3:30 P.M.; Join Claire Anna Jennings’ online defense
Project: Developing an Entertaining Choose-Your-Own-Adventure Game Illustrating Personal Cybersecurity Awareness

Cybersecurity awareness and education improvements in professional settings substantially increased with the development of serious cybersecurity games. These games engage interactively with the player to teach awareness and skills around cybersecurity and show higher engagement rates over other types of corporate training. These games primarily train, not entertain, and the average person chooses entertainment over voluntarily playing a serious game. While 93% of American adults use the internet and 72% use social media, most Americans approach cybersecurity with fear, confusion, or apathy, resulting in a reluctance to find training for personal cybersecurity awareness and education. This project combines entertainment and cybersecurity awareness by using a popular Hollywood storytelling approach, Save the Cat, with cybersecurity lessons to create an entertaining useful game. Iterative development, informed by user studies, ensures the game maintains a “fun” factor while improving the player’s cybersecurity awareness. Integrating cybersecurity knowledge into stories, with the primary goal of entertaining, allows the average American adult to improve their personal cybersecurity through a positive experience. Based on project results, not all useful games need to be serious games, and the average person does not need to seek cybersecurity knowledge to gain awareness.

Friday, August 11


Chair: Dr. Yang Peng
Candidate: Master of Science in Cybersecurity Engineering
5:45 P.M.; Join Harshavardhan Kakarla’s online defense
Project: Quick Connection/Handoff In An Opportunistic Vehicular Edge Computing Environment

The rise of connected vehicles has led to an explosion of data generated by vehicular networks. However, transmitting this data to centralized cloud servers can result in high latency and network congestion. Vehicular Edge Computing (VEC) presents a promising solution by offloading data processing to edge servers situated in close proximity to vehicles. This project introduces a VEC project that focuses on optimizing data offloading in connected vehicles, with a particular emphasis on quick connection and seamless handoff to ensure efficient edge computing in opportunistic environments.

The objective of this project is to design and implement a VEC framework that facilitates rapid data connection between connected vehicles and edge servers. The proposed system employs intelligent context-aware decision-making algorithm to dynamically choose edge servers based on network conditions, performance parameters, and vehicle proximity. By adopting this approach, the project aims to decrease data transmission time, reduce handoff delays, and enhance overall data processing efficiency, contributing to improved performance in opportunistic vehicular edge computing environments.

The project leverages Internet of Things (IoT) communication protocols, including MQTT, to establish real-time connections between the client, cloud service, and edge servers. Multiple algorithms are used to optimize edge server selection for seamless handoffs. Android devices and ESP32 microcontroller modules act as clients and edge servers, while AWS IoT Core and DynamoDB serve as cloud services respectively. The implementation involves Java for Android application development and C++ for Arduino IDE programming.

Comprehensive experimentation and testing of the VEC framework have been conducted by downloading the APK to multiple android devices which were used as clients and ESP32 microcontroller as the server. The results showcase significant improvements in connection and handoff delays. The system demonstrates exceptional performance in managing varying network conditions and ensuring seamless data processing at edge servers, enhancing the overall efficiency of connected vehicles.

The project’s findings highlight the effectiveness of Vehicular Edge Computing in addressing the challenges of connection handoffs in opportunistic environments. By optimizing data connection and enabling seamless handoff, the VEC framework contributes to faster data processing, reduced latency, and enhanced overall network efficiency. The project’s emphasis on quick connection and handoff in an opportunistic setting underscores the potential of edge computing to cater to the dynamic nature of vehicular networks, making it a valuable contribution to the field of connected vehicle technology.

Future endeavours focus on extending the VEC framework to accommodate a larger number of connected vehicles and edge servers, enabling scalability for city-wide smart transportation applications. Additionally, the integration of advanced handoff algorithms and context-aware decision-making techniques could further enhance the system’s performance and adaptability in opportunistic environments. Conducting field trials with live vehicular data would validate the framework’s effectiveness in real-world scenarios and provide insights into its practical deployment.

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Master of Science in Electrical Engineering

Spring 2023

Friday, August 11


Chair: Dr. Seungkeun Choi
Candidate: Master of Science in Electrical Engineering
11:00 A.M.; Join Pritam Bhandari’s online defense
Thesis: Characterization of Nafion Based Resistive RAM Devices

The development of computers in the modern era has escalated the race towards the development of powerful and efficient memory devices. In the past hundred years, we have gone from using punch cards for a mere kilobyte of storage to an enormous capability of storing terabytes of data. This progress has been mainly possible due to the advancement in the field of non- volatile memory devices and the fabrication technologies. By the use of advanced miniaturization techniques and new materials, we have been able to dramatically reduce the size of the memory devices while increasing the storage capacity and computing performance.

Despite having achieved this feat of miracle, we are reaching a point of slower growth in the computing performance of MOSFET-based non-volatile memory devices. It becomes very difficult to further decrease the size of memory devices. Hence, the next generation memory technology must have the following features to meet the high computing performance in the era of artificial intelligence: low-power consumption, fast switching, non-volatile, high-density fabrication. Resistive Random-Access Memory Devices (ReRAM) meets all those requirements; hence, is considered as one of the best candidates for the next generation memory technologies.

In this research, a ReRAM device with Nafion as a switching layer was fabricated. To characterize the resistive switching performance, Nafion was annealed at three different temperatures: 30°C, 60°C, and 90°C. In order to study the effect of different electrode, we used two different bottom electrodes (Au and Cu) and Al as a top electrode. The devices with Cu as a bottom electrode exhibits good resistive switching properties while the device with Au as a bottom electrode shows little or negligible switching performance. We found that the performance of switching is best when Nafion is annealed at 60°C. However, the experiment shows a wide variation of device performance even in the same substrate, indicating the importance of uniform film thickness and quality of Nafion.

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