Global Hawk Solutions

Qiskit, IBM's quantum computing SDK, to create a conceptual model of a digital market. The core idea is to frame a market analysis tool not as a simple data query, but as an act of measurement that collapses a "quantum-like" state of infinite market possibilities into a single, observable reality. Developed as part of X_Prize Quantum Initial Proposal Submitted to X Prize March 31st, 2025

Suite of Python scripts for the comprehensive analysis of IoT sensor data collected from a livestock environment. The project showcases an end-to-end data science workflow, from initial exploratory data analysis (EDA) to advanced statistical modeling, designed to extract meaningful insights from noisy, real-world time-series data.

AgriSearch is a full-stack Django application that provides a powerful semantic search interface for a custom-built database of agricultural extension publications. The system deploys a suite of sophisticated web scrapers to aggregate, process, and index thousands of documents from multiple Land Grant university websites, making a vast and fragmented body of knowledge easily accessible.

Python scripts that apply financial theories and modeling techniques to solve complex corporate finance problems. The scripts serve as practical, hands-on examples of how to analyze financing decisions, evaluate derivatives, and use simulation to understand financial outcomes.

The application leverages a powerful backend built with FastAPI, SQLAlchemy 2.0 (async), and Google Gemini Pro, with a dynamic, server-rendered frontend powered by Jinja2 and HTMX.

A full-stack Django application that acts as an intelligent agent, using a local LLM and Google's APIs to deliver a strategic playbook in minutes, not weeks.

Web scraping, agricultural science, and the NOAA API to create a tool that optimizes cattle feed costs based on environmental data.

Manually surveying and mapping physical infrastructure is a slow, expensive, and often dangerous job. Whether it's utility poles, cell towers, or city assets, someone has to physically go out and record the location of each item. But what if they didn't?

You don't always need a massive, multi-billion parameter AI model to solve complex NLP problems. For domain-specific tasks, a well-crafted, rule-based system with clever heuristics can be incredibly effective and efficient. By breaking down the problem—isolating text, handling word variations, and understanding negation I was able to turn a mountain of messy PDFs into a clean, actionable dataset.