I produced a professional STRIDE-based threat model for The Grand Marina's HYDROLOGIC IoT system, summarizing assets, CIA priorities, and top risks for nontechnical stakeholders.

I built a Python sensor log generator and supporting Colab notebooks while practicing data structures, control flow, functions, Pandas, and JSON handling for IoT security analysis.

I mapped an MQTT topic hierarchy for The Grand Marina and analyzed what an attacker could observe or do on exposed topics.

I wrote a professional security assessment report that summarized TLS deployment, experiment data, and concrete recommendations for the hotel's guest-management system.

I wrote a device provisioning policy and procedures for The Grand Marina Hotel's water monitoring system.

I wrote a Defense Comparison Report summarizing defenses against replay attacks and recommending a deployment strategy.

I compiled a Week 1 playbook and ran first SQL queries to answer key revenue questions from six raw data sources.

I built dim_product, joined the site, cart, and orders tables, and produced a product performance summary to support a reorder decision.

I built a Q4 ad allocation plan that connected Meta ad spend to revenue and recommended campaign budget changes.

I analyzed checkout data to detect bot-driven noise, built a filtered view, and recalculated the true cart abandonment rate for the externship project.

I built a Prompt Node that converted simple text instructions into structured prompts for an image agent, and documented the prompt examples used.

I delivered an end-to-end Consumer Trend Discovery Agent for washable rugs, including documentation, workflow screenshots, and a demo recording.

I built a Competitor Monitoring Agent workflow that fetched and merged product data from Wayfair, Amazon, and Walmart, then ran analysis and assembled a report.

I submitted an enhancement plan that expanded the agent's data inputs and generation tools.