Our client, Trailspur Capital Partners, is a real estate investment company based in Texas. We assist the company by building a database about commercial / industrial real estate to manage the market more easily and better decision-making. The business requires both the Geographic data from the County’s officials and the properties listings with vacancies information. Our goal is to design a database that can handle the aggregate data coming from both sources, which includes arranging and categorizing the properties, coming with several built-in functions namely identifying listings / vacancy changes, before deploying everything to the server. Our frontend, built with Vite and Vue, provides a smooth and interactive user experience while on the backend, we utilize AWS Lambda with Python to automate essential tasks, including downloading official county appraisal data, performing spatial merges using GIS functions, and managing our Supabase database. After successfully aggregating real estate data from both sources into a structured database, which enables easier tracking of property status changes, the platform efficiently processes and visualizes real-time property listings, allowing our client to analyze market trends and make data-driven investment decisions. Our project enhances real estate market intelligence for Trailspur Capital Partners. The system’s automated functions minimize manual workload and improve the accuracy of property tracking, providing a scalable solution for future expansion.

Cognitive Behavioral Therapy often relies on patients consistently completing therapeutic homework, regularly assigned by their therapist. A leading cause of Cognitive Behavioral Therapy failure for patients is non-compliance with their assigned therapeutic homework. About 20%-50% of patients fail to complete assignments due to inconvenience, a lack of clear instructions, or forgetting to finish the assignment. MENDmate is an online platform designed to solve this problem by providing a streamlined user experience for homework assignment and completion. MENDmate allows providers to assign homework to their patients and monitor their progress. It also provides patients with the ability to track and complete their homework assignments. Additional features of MENDmate include a learning library that gives patients an opportunity to learn about mental illness and practice coping techniques, a journal that allows patients to record their experiences and daily mood and anxiety assessments. MENDmate also tracks and reports the patient's data trends such as completed assignment, mood level, and anxiety level, which allows both the therapist and the patient to keep track of their progress over time.

Fort Worth PsychWorks, a leading psychiatry office, provides comprehensive neuropsychological and psychological assessments for patients across all age groups. Currently, after administering a variety of cognitive and behavioral tests, psychiatrists must manually input the resulting data into report templates, a process that is both labor-intensive and inefficient. This manual approach can take between 45 minutes to two hours per report, detracting from the time available for direct patient care and reducing the clinic’s overall operational efficiency.
To address this challenge, our senior design project introduces an automated report generation system named the PsychWorks Report Generation System. This software solution empowers psychiatrists to select or customize templates tailored to individual patient needs, add or remove specific tests, and automatically generate detailed report text, tables, and charts based on input scores. By maintaining the flexibility of the existing Excel-based system while significantly reducing the time and effort required for report creation, the PsychReport Automator enhances the user experience for clinic staff, boosts the potential for billable hours, and ultimately supports improved patient outcomes.

Obesity disproportionately affects underserved communities due to systemic barriers such as limited healthcare access, socioeconomic challenges, and a lack of culturally relevant health resources. Under the leadership of Dr. Christina Robinson and her team of medical students—Rumaila Hussain, Kavita Patel, Joice Song, and Fatema Jafferji—we are developing a mobile health application designed to support individuals in managing their health more effectively. This app will provide users with tools to track biometrics, manage chronic conditions, and set SMART (Specific, Measurable, Achievable, Relevant, Time-bound) health goals. A key feature of the app is a motivational text messaging system that encourages users to stay engaged with their health objectives. By integrating personalized and accessible interventions, this project aims to bridge healthcare gaps and empower individuals to take proactive steps toward healthier lifestyles.

The rapid advancement of artificial intelligence (AI) presents a unique opportunity to revolutionize education through personalized learning experiences. Traditional teaching methods often fail to address the diverse learning needs of students. This research explores the application of AI in education, focusing on machine learning algorithms, intelligent tutoring systems, and adaptive learning models to create personalized educational experiences. By analyzing student data, AI can optimize learning pathways, improve comprehension, and enhance engagement. The study discusses the potential, challenges, and future directions of AI-driven personalized education.