Thesis/Project Final Defense Schedule
Join us as the School of STEM master鈥檚 degree candidates present their culminating thesis and project work. The schedule is updated throughout the quarter, check back for new defenses.
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Master of Science in Computer Science & Software Engineering
SPRING 2025
Monday, May 19
SAVANNA (sAV) WHEELER
Chair: Dr. Marc Dupuis
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.; 56吃瓜网2 (Commons Hall) 327
Thesis: Investigating The Relationships Between SNS Usage, Personal Information Disclosure, And Cybersecurity
As social networking sites (SNSs) become ubiquitous for daily activities, users and regulators have raised concerns with the state of digital privacy and security. Cyberattacks on SNSs have exposed private data of millions of users, and cybersecurity threats propagate through social engineering over SNSs. To determine whether frequent SNS users who disclose personal information are at greater risk of cyberattacks, I conducted a hybrid survey-interview study measuring the correlations between personal information disclosure, SNS usage, cybersecurity practices, and past experiences with cybersecurity threats. The survey findings (n = 276) suggest that SNS usage frequency and usage for MNPS (meeting new people and socializing) or MEPO (make, express, or present more popular oneself) purposes have positive correlations to personal information disclosure. SNS usage frequency and personal information disclosure also had positive correlations with experienced cybersecurity threats, but little correlation with cybersecurity behavior. Interview responses highlighted how subjects experienced cybersecurity threats within SNSs.
Thursday, May 22
ELIAS MARTIN
Chair: Dr. Afra Mashhadi
Candidate: Master of Science in Computer Science & Software Engineering
1:15 P.M.;
Project: AuditAI – An Interactive Platform to Audit Question-Answer (QA) Pairs from AI and LLM Agents
AuditAI is an interactive platform to help users audit a question-answer pair that they received from conversations with an artificial intelligence (AI) agent.
This tool is especially relevant to current happenings in the world, as large language models (LLMs) and other AI agents become more prevalent and used in many contexts globally. In my capstone, I built out a framework for users to be able to gain insight into whether their question-answer pair is accurate and appropriate, taking into account machine learning (ML) models combined with other sources of content from the internet as a ground truth (using Google Search to fact check answer).
The goal of AuditAI is not to definitively say whether a question-answer pair being audited is correct or incorrect, but to give the user unprecedented access to information at their fingertips to come to their own conclusion about the information they are trying to audit.
PRATHAMESH PRAKASH BHALANGE
Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.;
Project: Peer-to-Peer Experience Synchronization System: Enhancing Communication and Control through Chat, Video Streaming, and Admin Features
As the demand for real-time collaboration and media sharing tools has increased in the past decade, the development of real-time innovative communication platforms capable of delivering a seamless communication experience has risen rapidly. Traditional systems rely heavily on centralized infrastructures to maintain coordination across distributed clients. While centralized architecture is effective for smaller-scale deployments, it poses significant challenges in scalability, performance overhead, higher operational costs, and infrastructure complexity.
This project presents a Peer-to-Peer Experience Synchronization System, implemented as a browser extension, that leverages decentralized communication using WebRTC to enable scalable, real-time synchronization without depending on persistent server connections. This project introduces session creation and connection establishment through a lightweight signaling server, which minimizes asset exchange cycles and reduces the server communication burden.
Beyond synchronization, the system integrates a robust communication framework designed to improve user interaction and control. Core features include real-time communication chat functionality, a two-person video conferencing enabling face-to-face video with synchronized audio collaboration, and admin access control mechanisms embedded within the browser extension UI.
By extending the communication mechanisms leveraging WebRTC and unifying these features within a single browser extension, this project demonstrates a holistic approach to decentralized streaming and communication. The system not only reduces infrastructure dependency but also enriches user experience through integrated tools for interaction and control.
VIVEKANANDA REDDY LENKALA
Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
5:00 P.M.;
Project: BuzzMagnet: A Bidding Ecosystem for Democratizing Access Between Social Media Promoters and Marketing Opportunities
In today’s digital landscape, social media usage has grown exponentially, creating opportunities for influencers to reach massive audiences through innovative approaches. However, many influencers struggle to establish sustainable partnerships with businesses that would allow them to monetize their content creation efforts. Simultaneously, businesses face challenges identifying appropriate influencers whose audience and style align with their brand values and marketing objectives. BuzzMagnet addresses this market inefficiency by creating a transparent bidding marketplace where businesses post advertising opportunities and influencers can bid to secure these campaigns. This reverse-auction approach empowers businesses to evaluate multiple potential influencers based on their proposals, while giving influencers of all sizes equal opportunity to showcase their value beyond mere follower counts. The platform provides comprehensive analytics to business owners, enabling data-driven decisions when selecting influencers based on relevant metrics such as engagement rates, and previous campaign performance. By democratizing access to advertising opportunities, BuzzMagnet particularly benefits emerging influencers who might otherwise be overlooked in traditional influencer marketing approaches. For businesses, the platform offers the advantage of discovering high-potential content creators at earlier stages of their careers, potentially securing more authentic promotion at competitive rates. Through this innovative bidding system, BuzzMagnet aims to transform the influencer marketing ecosystem by facilitating more transparent, merit-based connections between businesses seeking promotion and the diverse community of digital content creators.
Friday, May 23
WonWhoo (Andrew) nah
Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
3:30 P.M.;
Project: Understanding Software Engineering Principles: Developing a PowerPoint Builder Framework for Luna mHealth鈥檚 Authoring System
Luna mHealth has evolved over time, but its architecture required refactoring to improve extensibility and maintainability. This project explores how software engineering principles can transform PowerPoint based educational content into a structured format in the Luna authoring system. At its core, PptxTreeBuilder is a modular framework built upon test-driven development that extracts and organizes essential elements from .pptx files into Luna content modules, ensuring efficiency, readability, maintainability, and scalability. Through this project, I learned how to build a clean architecture, and use lean development principles. Beyond its technical contributions, this system establishes a sustainable, extensible authoring platform, empowering future developers to refine and expand its capabilities.
Master of Science in Cybersecurity Engineering
SPRING 2025
Monday, May 19
JUI ANIKET BANGALI
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
1:15 P.M.;
Thesis: Evaluating the Impact of Responsible AI as a Security Control in Machine Learning
Responsible Artificial Intelligence (RAI) is an approach to developing and deploying AI systems in a manner that is ethical, trustworthy, and safe. While RAI is often framed in terms of fairness, transparency, and social responsibility, its potential role in improving the security and robustness of machine learning (ML) models remains underexplored. This research proposes that integrating RAI principles during the development lifecycle of ML models can serve not only as a foundation for ethical AI but also as a proactive security control. Using Microsoft鈥檚 Responsible AI framework, this study examines whether models built with responsible development practices are more resilient to adversarial attacks compared to those developed without them. The findings confirm that RAI practices significantly improve model robustness, with the improved model reducing the average accuracy drop due to adversarial attacks by 46.23% compared to the baseline model. Notably, this result was achieved without applying any additional security-specific defenses, demonstrating that RAI alone can serve as an effective and independent layer of protection. This positions RAI not only as an ethical imperative but also as a practical, adaptable defense mechanism that can complement existing security techniques, offering valuable guidance for AI practitioners building trustworthy and secure systems.