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- Creators: Computer Science and Engineering Program
Description
Accurate drone localization in urban environments remains a challenge due to GPS signal blockage, multipath interference, and unreliable vertical positioning caused by dense architectural structures. This research investigates an alternative approach using Immersal’s visual positioning system (VPS) to enable image-based localization without relying on simultaneous localization and mapping (SLAM) or ARFoundation for mobile devices. By adapting the Immersal pipeline to accept external camera input, this work simulates a drone-based setup using webcam footage and estimates focal parameters to support localization. While real drone deployment is outside the project scope, the resulting software provides a foundation for future integration with drone hardware by identifying the necessary sensor data for visual localization and connecting the necessary pipeline data. This approach lays the groundwork for infrastructure-free navigation in GPS-degraded urban environments, and the system has successfully demonstrated the ability to generate maps and extract camera poses using custom captured images run through Immersal. This was validated through webcam-based tests and offline drone footage, where Immersal returned consistent pose estimates and successfully built .ply-format spatial maps using synchronized image-pose data.
ContributorsColyar, Adam (Author) / Chavez, Maria (Thesis director) / Baillot, Yohan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This project presents a high-performance implementation of Gotoh’s pairwise sequence alignment algorithm within the scikit-bio Python library, optimized using Cython for computational efficiency. Designed to address the limitations of scikit-bio’s original pure Python alignment function, the new implementation achieves substantial performance gains—exceeding a 2800x speedup on longer sequences—while maintaining accuracy and full compatibility with the existing scikit-bio API. Emphasis was placed on modular code structure, enabling ease of use, maintainability, and seamless substitution between global and local alignment modes. Benchmarking against BioPython, BioTite, and the prior scikit-bio implementation confirms the new algorithm’s competitive runtime and practical value for real-world bioinformatics workflows. This work demonstrates the potential for integrating low-level performance enhancements within high-level, user-friendly scientific computing environments.
ContributorsAzom, Raeed (Author) / Zhu, Qiyun (Thesis director) / Aton, Matthew (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
The evolution of digital commerce has fundamentally reshaped the landscape of global
financial transactions, establishing an ecosystem where speed, operational efficiency, and user convenience are the highest priority. This transformation, driven by the adoption of
internet-based platforms and mobile technologies, has enabled unprecedented levels of
accessibility and connectivity in economic exchanges. However, this rapid expansion has caused a significant escalation in payment fraud, which exploits vulnerabilities inherent in contemporary transaction systems. Historically, platforms such as Stripe Radar have adopted a centralized approach to fraud prevention, aggregating extensive datasets—including credit card details, behavioral analytics, and device fingerprints—to identify and mitigate fraudulent activities. This methodology, while effective in certain contexts, introduces substantial risks due to the concentration of sensitive information within a single architectural framework. Such
centralization renders these systems prime targets for cyberattacks, as evidenced by high-profile breaches that have compromised millions of users’ personal and financial data.
ContributorsGundala, Revanth (Author) / Boscovic, Dragan (Thesis director) / Bazzi, Rida (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Economics Program in CLAS (Contributor)
Created2025-05
Description
Machine learning has increasingly played a pivotal role in societal decision-making. In such contexts, ensuring the fairness of models becomes critically important. Unfortunately, prior studies have shown that without intervention, models often inherit and even amplify biases present in the datasets. Existing group fairness metrics, such as true positive rate parity and equalized odds, primarily focus on the relationship between model predictions and ground truth labels stratified by sensitive attributes. However, most existing notions of fairness overlook whether the underlying rationale of a model’s decision-making process varies across different subgroups. To fill this gap, we propose a novel metric, FIDSHAP, which evaluates fairness through explainability by quantifying discrepancies in the model’s decision rationales across groups. Subsequent experiments and optimization procedures validate the effectiveness of this metric and underscore the potential of addressing fairness from the perspective of explainability.
ContributorsQiu, Ziyue (Author) / Choi, YooJung (Thesis director) / De Luca, Gennaro (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Many small to mid-sized businesses face significant challenges in integrating artificial intelligence (AI) effectively, primarily due to a lack of internal expertise that bridges technical capabilities with core business operations. This gap leads to operational inefficiencies, increased costs, and potential competitive disadvantages. CAIO Labs addresses this critical issue by providing specialized AI consulting coupled with tailored training programs designed to empower internal team members as dedicated Chief AI Officers (CAIO). Through detailed operational audits and strategic training, CAIO Labs enables sustainable AI-driven transformations, significantly reducing dependency on costly external consultants. By specifically targeting businesses within professional services and e-commerce sectors, CAIO Labs positions itself uniquely in the market, offering personalized solutions that yield measurable improvements in productivity and scalability. This thesis outlines CAIO Labs' strategic approach, validates its core business assumptions, and demonstrates the viability and impact of embedding internal AI expertise within growing businesses.
ContributorsBhangale, Parth (Author) / Byrne, Jared (Thesis director) / Dearman, Jeremy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Paris, renowned for its culinary heritage and multicultural landscape, offers a unique setting where diverse gastronomic traditions intersect. Among these, Asian food options occupy a significant and growing presence, inviting questions about how cultural identity is both showcased and perceived within the framework of French society. This research seeks to answer the question: How do visitors perceive Asian food options in Paris as reflections of cultural identities within the context of France's secular ideals? By examining visitor perceptions, the study aims to uncover the ways in which food becomes a site of cultural dialogue, adaptation, and sometimes tension, within the broader context of France’s approach to diversity and secularism.
ContributorsMarria, Shreya (Author) / Briggs, Georgette (Thesis director) / Foy, Joseph (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
In an age of quickly growing technology, a persistent need exists for code that, while functionally similar, is tailored to specific use cases across websites, applications, and software systems. With the rise of powerful large-language models (LLMs) such as ChatGPT, Claude, Copilot, and more, there is growing potential to reduce redundant coding by reusing and adapting existing solutions. This project explores Microsoft’s PowerApps, Azure, and Google Cloud, which are three platforms that have suites of services that can create complex full-stack software using a more functional approach with an emphasis on programmatic simplicity, ease of connection, utilizing endpoints and API calls, and visual programming. Specifically, the variety of essential building blocks that Azure, Google Cloud and PowerApps provides and their use-cases to emulate an application built for our Capstone project: a chatbot teaching assistant that scrapes information from canvas shells; vectorizing them and utilizing a similarity search to feed relevant pieces of information into the large language model to provide contextually relevant answers related to available information within the course. Throughout the process of building the application through PowerApps, differences between how various functions are implemented in a traditionally built application using mainly Python, Qdrant vector database, PostgreSQL, and other API libraries will be studied, compared, and contrasted to the functionalities provided by the services offered by PowerApps and cloud platforms. This analysis reviews the viability of no/low-code development in the modern software development scene and demonstrates that core-level functionality can be fully integrated using these Low-Code software development environments.
ContributorsSingh, Archit (Author) / Feng, Gregory (Co-author) / Chen, Yinong (Thesis director) / De Luca, Gennaro (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
In this project, my partner Archit and I looked to explore the future of no-code/low-code development. Throughout this project, we initially researched low-code/no-code development environments before settling on Microsoft Power Platform and Google Cloud to implement our capstone project, a retrieval augmented generation large language model teaching assistant that uses course-specific content pulled directly from Canvas and fed into the model. At the end of this project, we were able to fully replicate our capstone project's base frontend and the majority of our project's backend within two weeks, with a functional chat screen, two-factor authentication using a verification code, and a built-in user statistics report as compared to our capstone project, which took a whole semester to create.
ContributorsFeng, Gregory (Author) / Singh, Archit (Co-author) / Chen, Yinong (Thesis director) / De Luca, Gennaro (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
The Computer Science(CS) students who attend Arizona State University(ASU) are required to participate in a capstone project. For two semesters, they work on a team with other students to help a sponsor who has requested some work to be completed by the students. In some cases, this results in a group of young students, typically ages 20-22, creating a project intended for an audience much older than themselves, 45 and above. This can pose specific challenges as younger computer users can overestimate others' familiarity with technology. Students may create designs they feel are well crafted, which fail to account for an older demographic’s detached web browsing experience. With this situation in mind, a survey was created to test the web design sensibilities within a group of college students and a group of people 30 years their elders. When analyzing the results, the answers didn’t display a large discrepancy across age groups, however, the free response sections showed a large divide between the age groups. The 18-24 age block conveyed a greater familiarity with the technology they use. They’re more confident in their ability to use the tools provided to them compared to the 45+ age block whose short answers display a hesitant attitude toward the computer. This disconnect was further exemplified by survey questions, which resulted in short and unhelpful answers.
ContributorsEllis, David (Author) / Malpe, Adwith (Thesis director) / Dorsey, John (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2025-05
Description
A natural language processing (NLP) chatbot is a program that can communicate with a human by processing their language into understandable commands. While most associate AI with LLMs, these models are not as effective with specific, involved tasks. The goal of this thesis is to demonstrate how NLP can be combined with a small-scale generative AI model to create a chatbot that can complement larger projects. The thesis researches the benefits of a small-scale chatbot in contrast to larger models in cost, time efficiency, and accuracy, and it details an example of the implementation of a small-scale chatbot within a larger project. For the implementation, I have collaborated with my sponsor, Northrop Grumman, to integrate an NLP chatbot into their GSE Frontend project. The chatbot interacts with the user, requesting specific commands related to log history, graphing, and obtaining data from the main program. The result of the implementation is an effective tool that complements the main program’s purpose with little cost and error and has great expandability alongside the program to improve its functionality.
ContributorsKhondoker, Maheeb (Author) / Osburn, Steven (Thesis director) / Arora, Aman (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05