Project 1: Understanding IoT Systems & Threat Modeling

I analyzed the IoT systems at The Grand Marina Hotel, mastering the CIA Triad and identifying vulnerabilities through the STRIDE methodology. This foundational work culminated in a professional threat model that outlined potential attack vectors and informed my subsequent security strategies.

Project 3: Building an Insecure MQTT Pipeline

I constructed a complete IoT pipeline, including sensors, a message broker, and subscribers. By intercepting traffic, I gained firsthand experience of the vulnerabilities present in an insecure MQTT setup, enhancing my understanding of potential attack scenarios.

Project 5: Enforcing Device Identity & Provisioning

I enhanced the security of my TLS pipeline by implementing mutual TLS (mTLS), ensuring that both the broker and devices authenticated each other. This resulted in a provisioning policy document that outlined the criteria for device connections at The Grand Marina.

Project 7: Building a Real-Time Security Dashboard

I built a real-time security dashboard that displayed live sensor data and validated messages while providing visual alerts for potential attacks. This dashboard improved operational awareness and allowed for better response strategies during security incidents.

Project 2: Learning Python for IoT Security

I learned Python fundamentals and applied them to analyze IoT security data. This included data preparation techniques with JSON and Pandas, enabling me to write scripts that effectively analyzed HYDROLOGIC sensor data for security insights.

Project 4: Securing the Pipeline & Measuring the Cost

I secured the MQTT pipeline by implementing TLS encryption, ensuring that message signatures were verified. I conducted experiments to evaluate the trade-offs between security and performance, ultimately delivering a comprehensive security assessment for the hotel's management.

Project 6: Defeating Replay Attacks

I developed three layers of defense against replay attacks, including timestamp validation, sequence counters, and HMAC message signing. This ensured that my subscriber not only verified the sender's identity but also confirmed the freshness and integrity of each message.

Project 8: Adding AI-Powered Anomaly Detection

I integrated AI-powered anomaly detection into my existing dashboard, training an Isolation Forest model to identify subtle deviations in sensor data. This proactive approach enhanced the system's ability to detect threats that bypassed traditional rule-based defenses.

Project 1: Getting Into Breaking Games' Raw Data

I collaborated with Jess Walker to analyze six disorganized data sources, setting up a SQLite database. I wrote initial SQL queries to extract revenue data, significantly reducing the time to answer business questions from two hours to thirty seconds.

Project 3: Following the Money

I created a denormalized dashboard table for real-time reporting and built a static HTML dashboard using Chart.js. I prepared a narrative and a 10-minute presentation to showcase my findings, successfully presenting the Holiday Game Plan to the team.

Project 5: Building Breaking Games' Holiday Dashboard

I investigated cart abandonment by generating hypotheses and designing a clean_checkouts view to filter out noise. I recalculated the true abandonment rate and delivered a diagnosis report with actionable recommendations to improve conversion rates.

Project 2: Designing Your First Database

I designed a new database schema that unified data from multiple spreadsheets. By creating a clean dimension table and implementing primary and foreign keys, I provided a comprehensive view of customer journeys and delivered a reorder recommendation for holiday products.

Project 4: Investigating Breaking Games' Cart Abandonment

I investigated cart abandonment by generating hypotheses about customer behavior and designing a clean_checkouts view to filter out noise. I recalculated the true abandonment rate and delivered a diagnosis report that separated signal from noise, providing actionable recommendations for improvement.

Project 1: Getting Started with AI Agents - Moodboard Generator

In this project, you’ll build your first AI agent: a Moodboard Generator that turns simple prompts like “modern living room with warm colors” into visuals. By setting up your workspace in n8n and running your first workflow, you’ll see how ideas can instantly become outputs. What feels like a creative experiment is actually the foundation of Wayfair’s edge: turning abstract trends into concrete visuals fast. This hands-on win sets you up to build the more advanced agents that will track market shifts, monitor competitors, and generate insights that matter for Wayfair..

Step 1: Install n8n + Pick Your Rug Category

I installed n8n locally, enabling limitless building capabilities. I explored Wayfair's rug categories and selected one that excited me for further analysis. This preparatory step ensured that my workflow outputs were focused and relevant.