Love, Cat & Robot is an RPG game that combines emotional storytelling with retro aesthetics. Players step into Tyra’s life, navigating her daily routine and uncovering the hidden truths of a mysterious world. Through interaction with two central NPCs—Tyra’s pet cat and Derek, an AI assistant, players are allowed to shape the direction of the story, unlocking different endings based on their decisions. As the plot evolves, the game transforms from a peaceful routine into a haunting experience filled with metafictional twists and darker undertones, keeping players immersed and eager to discover the true endings.

Developed in Unity, Tyra features pixel art backgrounds to evoke a nostalgic yet atmospheric setting. The gameplay blends exploration, branching dialogues, and mini bullet-hell combat. Players use WASD keys for movement and the mouse for interactions, seamlessly shifting between contestant-based dialogue choices and avatar-based combat sequences. This hybrid interaction model ensures a varied and engaging experience, balancing narrative depth with gameplay challenge.

Waterra is a web-based platform developed under the Ohneganos program to monitor water quality. It features a functional map that allows users to explore real-time environmental data, such as pH, turbidity, etc., which are collected from water quality sensors. Designed with accessibility in mind, the platform automatically generates intuitive line graphs to help both professionals and community members interpret trends.

The platform was built using React and TypeScript, with a custom UI component library designed to present sensor data on a color-coded Leaflet map for geographic and temporal analysis. Backend APIs were developed in Go to query sensor data stored in InfluxDB, a time-series database linked to remote monitoring hardware.

Bridge Chloride Exposure Predictor (BCEP) is a scientific software tool that forecasts corrosion risks on highway bridges by modeling chloride exposure from deicing salts like sodium chloride. BCEP helps stakeholders, such as government agencies, assess exposure levels and plan maintenance budgets accordingly, allocating more resources to areas with higher predicted chloride exposure.

Built as a web application using Vue.js, HTML, and CSS, BCEP delivers visualizations of chloride exposure predictions through an intuitive user interface. Python was used to construct the backend database and generate prediction trends based on climate and traffic datasets. The project incorporates Continuous Integration via GitHub Actions for streamlined testing, while stakeholder feedback is managed through GitHub Issues. A tailored requirement validation process ensures ongoing alignment with user needs and supports long-term maintainability.