Other Projects
Evaluating Loan Eligibility with Machine Learning
Early, Exploratory Project
Built and audited a credit-risk classifier for loan approval.
Identified biased outcomes caused by compromised training data that standard performance metrics failed to reveal.
Added an interpretability layer to trace individual predictions back to the features driving each decision and isolate the failure mode.
Demonstrated that a model can appear highly accurate while still producing decisions that users and auditors cannot understand or contest.
This project became the foundation for my later research on explainable and accountable machine-learning systems.
GAME TITLE:
Under Ordinance
Independent Game Development
Under Ordinance is a first-person combat and exploration game, spanning worlds fractured by authoritarian governance. Built around AI enemies and modular environments, each planet operates under a different system of control, requiring players to adapt to unfamiliar terrain and fresh mechanics.
Player enters Palewood Forest
Designed and implemented core gameplay systems in Unity (URP), including movement, combat logic, and enemy behaviors.
Iteratively refined mechanics in C# through testing and player feedback across multiple versions.
Tuned difficulty, progression, and the feel of PvE interactions to balance user experience and system stability.
Sustained independent development over several years, returning to the project as my skills evolved.