Leveraging Statistical Learning and Large-Scale Data Architecture to Secure the Global AI Supply Chain.
Applying supervised and unsupervised learning to decode complex behaviors within high-volume datasets. My current research focuses on dimensionality reduction and model validation—essential tools for auditing the compliance of frontier AI models.
Analysis of distributed data storage architectures and the protocols required for cross-border data integrity. This work explores the intersection of SQL/NoSQL database management and the physical security of data at rest.
A strategic framework for the future of AI sovereignty. This project explores the use of hardware-level telemetry and 'Proof of Location' to enforce compute export controls, specifically addressing the 50,000-chip threshold established by the 2025 Diffusion Rule.
Institution:
Ball State University (MS in Data Science)
Current GPA: 4.0
Key Coursework:
Machine Learning & Data Mining (CS 654)
Data Storage & Management (DSCI 604)
Categorical Data Analysis (DSCI 686 – Upcoming Summer '26)
Statistical Learning (MATH 624 – Upcoming Fall '26)
Languages:
Python (Scikit-Learn, Pandas, NumPy), R, SQL.
Specializations:
Linear Regression, Cluster Analysis, Database Architecture, Compute Governance.