Public property tax data is often presented in raw formats, making it difficult for the average user to interpret. Our client initially developed a product that provided access to Kern County property tax information only. Our project enhances accessibility by developing ParcelSearch.com, a platform that centralizes property tax data. With this rebranded system, we have expanded coverage to include Kern, Monterey, and Tulare Counties, with plans for further expansion. Users can create accounts and choose from various subscription plans to conduct property searches using multiple search criteria: owner name, parcel number, and legal descriptions. With the development of a user-friendly interface and expanded search functionalities, the platform caters to realtors, investors, and homeowners seeking property insights. This system was built using modern web technologies, including Vue.js for the frontend, Java and Spring Boot for the backend, and PostgreSQL for database management, to name a few. Future plans include expanding nationwide to create an all-encompassing and user-friendly property data platform.

Longspeeds is an innovative e-commerce platform designed to streamline the buying and selling of auto parts, providing a seamless experience for both individual customers and automotive businesses. The platform offers a comprehensive catalog of high-quality parts for a wide range of vehicles, from everyday cars to performance and specialty models. Leveraging modern web technologies such as Next.js, Longspeeds ensures fast performance, responsive design, and secure transactions. Key features include advanced search and filtering, user-friendly inventory management, real-time order tracking, and support for both retail and wholesale transactions. With a focus on reliability, affordability, and user satisfaction, Longspeeds aims to become a trusted destination for auto enthusiasts and mechanics alike.

Academic advising presents significant challenges in both time management and complexity. Currently, students navigate between two advising options: generic online resources and personalized consultations with professors and advisors. While personalized advisement offers tailored advice, professors cannot be expected to meet with every undergraduate in their major, especially as enrollment grows, and academic advisors may lack specialized knowledge required for some majors. Echelon addresses this gap by creating a middle ground between generic and personalized advising, offering students an effective supplement and saving time for all parties involved. Echelon functions as an intelligent chatbot assistant powered by large language models such as Llama 3 and Mistral. Upon signup, students share their academic records, enabling Echelon to create individualized profiles that consider key factors such as major/minor selection and performance in critical courses. The project is being built using TypeScript and Rust with Svelte and Axum frameworks, respectively. Echelon utilizes PostgreSQL for user account and conversation storage and Qdrant for vector storage and retrieval. Designed with flexibility in mind, Echelon can be deployed at any university, given basic institutional information such as course catalogs and degree requirements.

Cryptococcus is an invasive fungus that causes cryptococcosis, an infection that highly affects immunocompromised people. There are currently a limited number of antifungals available to treat Cryptococcus, and with the increased in antimicrobial resistance, we need different alternatives to treat fungal infections. Our lab has identified proteins involved in the synthesis of eicosanoids, which are lipid signaling molecules involved in regulating the immune response. Moreover, fungi can produce eicosanoids using different enzymes that humans do, opening a line to identify new drug targets using these pathways. Previously, our lab had identified genes upregulated in the presence of the eicosanoid’s precursor, arachidonic acid. Our goal is to use bioinformatics to predict and characterize the protein structure, using AlphaFold2, a machine learning application based on a deep neural network, and using this tool, identify small molecules that will bind to the proteins and help make drug design more efficient.

FWDX BioBlade is a web-based system designed to automate genetic sequence comparison for Fort Worth Diagnostics (FWDX), a company specializing in high-quality diagnostic reagents. FWDX faces a significant challenge: ensuring its reagents remain effective as pathogens mutate over time. Currently, this process is manual, time-intensive, and costly, relying on external bioinformatic consultants to compare existing reagent sequences against national and international genetic databases like NCBI and GISAID.

BioBlade improves this workflow by automating sequence comparisons, detecting mutations or deletions, and generating real-time reports. This automation significantly reduces turnaround time, improves accuracy, and lowers costs, empowering FWDX scientists and regulatory personnel with timely and accurate information. Key features include:
- Automated sequence analysis for efficient reagent validation
- Customizable query intervals for up-to-date comparisons
- A user-friendly reporting dashboard for streamlined decision-making