A comprehensive collection of my software development projects spanning mobile applications, AI integration, bioinformatics tools, and machine learning implementations.
A cross-platform mobile application designed to connect BYU-Idaho students for safe, efficient, and community-driven ride-sharing. Built with Flutter and Firebase to provide a secure platform for riders and drivers within the BYUI community, addressing campus commute needs.
Successfully translated complex Tailwind CSS designs into idiomatic Flutter widgets, maintaining design consistency across platforms.
Implemented comprehensive user authentication states and data persistence with Firebase services.
Effectively handled UI state changes including password visibility, loading indicators, and navigation flow.
A custom Discord bot assistant that integrates with a locally hosted LLaMA 3 model via Ollama, designed to help manage daily tasks, reminders, and alerts in a natural and conversational way. Built to provide a persistent, sarcastic Alfred-style assistant with smart task management.
Hosting and querying a local LLaMA model while maintaining relevant, assistant-like responses and personality consistency.
Parsing natural language time input from users and converting to UTC reliably across timezones.
Designing a system for persistent, recallable memory and tasks with efficient SQLite storage.
A Python-based tool for designing SNP-specific primers that improve genotyping accuracy using mismatch logic and quality control filtering. Automates the entire primer design pipeline from sequence extraction to export-ready results, reducing manual errors in SNP-based PCR experiments.
Implemented controlled mismatches at exact locations to increase allele-specific amplification.
Designed comprehensive handling for both 5'-to-3' and 3'-to-5' sequences using reverse complements.
Built logic to find complementary primers for SNP amplification at specific genomic distances.
A comprehensive collection of machine learning projects showcasing various algorithms, techniques, and real-world applications across computer vision, regression, and classification tasks. Each project demonstrates different aspects of the ML pipeline from data preprocessing to model deployment.
Achieved 100% accuracy on critical STOP sign classification for autonomous driving safety requirements.
Integrated U.S. Census data with housing features to improve price prediction accuracy significantly.
Projects address practical problems in transportation, finance, and urban planning with actionable insights.