Agent-based models (ABMs) are widely used in computational biology to simulate complex processes such as infectious disease transmission. However, many research-grade models are implemented primarily as backend systems and lack graphical interfaces that allow researchers to efficiently configure simulations and interpret outputs. In collaboration with the Biophysics Department, this project focused on the development of a graphical user interface (GUI) for an existing viral agent-based simulation platform previously implemented without an interactive frontend.
The implemented interface integrates with the existing backend simulation environment deployed on laboratory systems, enabling structured parameter configuration, simulation execution, and visualization of model outputs. Development focused on frontend architecture, parameter validation mechanisms, backend connectivity, and data visualization components for simulation result analysis. Additional work included interface refactoring and codebase cleanup to improve maintainability and usability.
The resulting system provides a structured workflow for configuring and executing simulations while preventing invalid parameter configurations through input validation. By extending the existing modeling framework with a robust graphical interface and visualization capabilities, this work improves accessibility and operational efficiency for researchers conducting computational epidemiology experiments within the laboratory environment.

Machine Performance Check Plus (MPC+) is a software platform designed to improve quality assurance workflows for Varian TrueBeam linear accelerators used in radiation therapy. The system automatically collects and processes Machine Performance Check (MPC) data generated by clinical machines, including imaging files and measurement results, and converts them into structured, analyzable information. The platform provides a web-based dashboard that allows medical physicists and clinical staff to review machine performance metrics, visualize trends, and quickly identify values that fall outside acceptable tolerances. MPC+ also supports digital sign-off workflow and audit trails to ensure compliance with regulatory and clinical standards. By consolidating data from multiple machines and clinics into a single interface, the system reduces the time required for daily QA review while improving reliability and traceability. Overall, the project aims to make the quality assurance process more efficient, data-driven, and scalable for radiation oncology clinics operating Varian TrueBeam systems.

This senior design project centers on VANTAGE (Visual Autonomous Navigation and Task-driven Agentic Ground-to-air Engine), an AI-driven drone operations platform developed with MavenCode, a leading AI/ML solutions provider in the Dallas–Fort Worth area. VANTAGE enables users to command drones through natural language while integrating real-time perception tools such as speech-to-text, text-to-speech, object detection, semantic segmentation, and vision-language reasoning. The system combines a FastAPI backend, agent-based tool orchestration, and a web dashboard that supports both mission control and direct testing of AI tools through uploaded audio, image, and video inputs. Our team’s work spans full-stack development, model integration, and interface design to deliver a practical, user-centered platform for intelligent aerial autonomy. Together, these components demonstrate an end-to-end AI product approach that aligns with MavenCode’s mission of empowering organizations through training, product development, and consulting.

Large language models (LLMs) such as ChatGPT, Claude, Gemini, and Llama are increasingly being deployed as search and decision-support tools for health-related inquiries. As users provide demographic context, including age, to obtain personalized guidance, these systems can differentially adjust tone, directive strength, or safety framework. Although age can be clinically relevant, unintended variation in the generated advice can introduce systematic safety disparities or representational bias. In this study, we analyze outputs from two major LLM families across 10,679 physical and mental health scenarios to examine how explicit age cues, including teen, young adult, middle-aged, and senior, influence the safety and linguistic properties of generated health advice. To quantify these effects, we introduce three task-specific evaluation metrics: Age Differential Safety Bias (ADSB) to measure relative safety shifts under demographic conditioning, Safety Risk Score (SRS) to capture cumulative weighted unsafe recommendations, and Tone Differential Index (TDI) to detect systematic changes in linguistic complexity and formality associated with representational harm. The results indicate that explicit age cues systematically alter the behavior of the model. Demographic conditioning consistently reduces safety quality relative to age-neutral baselines. Middle-aged cohorts exhibit a higher cumulative safety risk in directive responses, whereas senior cohorts demonstrate elevated tone shifts consistent with oversimplification and increased formality. These findings suggest that demographic sensitivity can introduce measurable allocative and representational disparities in healthcare-oriented LLM systems. This work establishes a reproducible audit framework for evaluating demographic safety sensitivity in health-focused LLM deployments and contributes to the development of standardized evaluation protocols for safer and more equitable integration of AI systems in clinical and consumer health environments.

The BatLab project aims to develop a machine learning based tool that assists researchers in identifying bat species from acoustic recordings. Bats rely on echolocation calls that vary in frequency, duration, and shape, allowing species to be distinguished through analysis of their recorded calls. Currently, researchers must manually review large volumes of acoustic recordings, which is a time consuming process that limits the scale of ecological studies. This project explores the use of supervised machine learning to automate the classification of bat echolocation calls using labeled training data. The system analyzes acoustic features within recorded calls and predicts the most likely species while flagging uncertain cases for further review. In addition, the project focuses on improving data organization and providing a user friendly interface that allows researchers to efficiently visualize and manage acoustic data. By reducing the manual workload involved in analyzing bat call recordings, the BatLab system aims to support ecological research and improve the efficiency of studying bat populations.

This senior design project focuses on developing PostAgent, an AI-powered content creation platform created by Corevation, an innovations tech company. This product is aimed at helping businesses and entrepreneurs with creating and managing social media content more efficiently and allow marketing endeavors to be more manageable. Our team is building multiple features, including AI tools to regenerate and edit post text and images, an analytics dashboard for tracking social media performance, and a centralized content library for organization purposes and for users to upload custom content to the platform. We are also assisting in the overall UI/UX to ensure an intuitive user experience and developing a company website to support Corevation’s public presence. Together, these components demonstrate a full-stack approach to product development, blending AI capabilities with user-centered design.

How would one classify an apple fruit versus an apple phone? Typically as a fruit and a technology device. However some modern systems for classifying patents are insufficent and would be unable to differentiate between the two and cluster both based on their containing the word ‘apple’. Our task with iPELiNT is to build upon solutions to better visualize how USPTO( United States Patent and Trademark Office) art unit’s change over time. An art unit is a group of USPTO examiners specializing in a specific technology area. Our end product helped establish a data-driven system for conducting forensic analysis of USPTO patent examiner dockets using vector embeddings and internal data pipelines. We used mongoDB for our database, JavaScript and Python for our backend, and NuxtJS and Vue for our frontend. Our 5 phases of development are as follows. 1. Data Aggregation and Preparation. 2. Centroid Calculation and Art Unit Profiling. 3. Deviation Analysis and Scoring 4. Visualization and interpretation Framework.

Artificial intelligence tools, especially large language models (LLMs) are progressively being integrated into educational settings as resources that can enhance student learning and offer novel methods for information retrieval. As these technologies advance, educators and researchers are increasingly focused in comprehending their impact on student learning and engagement with academic content. This study examines the potential role of AI-based systems in facilitating student learning by analyzing various ways employed by students to obtain and process information during study activities.
The study's participants are split up into four groups, each of which accesses learning resources in a different way. The first group relies on traditional text-based study resources. The second group uses general online resources to gather information. The third group is allowed to use AI-based tools powered by large language models to receive explanations and assistance. The fourth group uses a hybrid strategy that blends AI-supported tools with conventional study materials.
The performance and learning experiences of these groups are compared to evaluate how different resources influence students’ understanding of course concepts. The findings are expected to provide insight on whether AI technologies can successfully supplement conventional teaching methods. Understanding these effects help educators determine how to appropriately incorporate AI and LLM tools into classroom settings to improve learning while upholding efficient teaching methods.

The Driving Safety Certificate Management System is a web application designed to streamline
the administration of driving safety courses in Texas. Currently, instructors conduct classes
independently but rely on the licensed provider to process student information, retrieve driving
records, and issue course completion certificates, which can cause delays and create additional
administrative work. This system shifts those responsibilities directly to instructors by allowing
them to manage classes, enroll students, process student information, and generate certificates
through a centralized platform. By automating these processes, the system reduces manual
workload, improves efficiency, and enables faster certificate delivery for students. The
application also maintains oversight for administrators while ensuring that instructors can
operate more independently within the requirements set by the Texas Department of Licensing
and Regulation.

This study analyzed droplet sizes generated by nebulizers by collecting aerosolized liquid on microscope test slides and processing microscope images with MATLAB to quantify droplet distributions. Measurements were compared to the target droplet size range required for effective nebulization, since droplets outside this range can reduce respiratory delivery efficiency. Results help evaluate nebulizer performance and ensure droplets meet specifications for optimal aerosol behavior.