Overview

HR Trends is a platform I built during my internship at Intrizen that turns public U.S. labor-market data into something a business can actually use day to day — tracking indicators like the unemployment rate, employment rate, and education-to-job ratio — instead of leaving that data sitting in static government reports.

The Problem

Government labor data from the Bureau of Labor Statistics (BLS) is public, current, and genuinely valuable — but it isn't built to be used. It's spread across static reports on its own update schedule, with no easy way to turn it into a live view of what's actually changing in the labor market. Intrizen needed a way to make that data continuously useful instead of a one-time pull.

Separately, on the marketing side, I found a version of the same problem on Intrizen's own LinkedIn page: good raw material — roughly 30 employees — that wasn't turning into visibility. Only 6 employees showed up as connected to the company page, and organic engagement was stuck around 30 people a month.

My Role

I was the sole Project & Marketing Intern on this, which meant I owned it end to end: the data pipeline, the website and admin dashboard, the AI layer on top of it, and the brand identity around it — plus the separate analysis of the company's LinkedIn performance.

Process

The pipeline pulls data from BLS, runs it through automated n8n workflows, and stores it in Supabase, which powers both the public website and an internal admin dashboard. On top of that, I built an agentic AI workflow that can generate content, visuals, and insight summaries directly from the underlying data.

What I Built

• Automated data pipeline (BLS → n8n → Supabase)
• Public website and internal admin dashboard
• Agentic AI workflow for generating content, visuals, and insight summaries
• Brand identity — logo and a sea-green visual identity for the platform
• A LinkedIn growth strategy proposal (employee advocacy + AI-generated content), based on the engagement analysis above

Outcome

HR Trends shipped as a live platform that Intrizen uses internally to pull labor-market insight into its marketing and business work. The LinkedIn analysis and strategy were delivered as recommendations — I don't have post-adoption engagement numbers to report yet, so I'm not listing any.

Reflection

Overview

As Secretary of Fair Lawn's municipal Green Team advisory committee, I spent over 100 hours turning unstandardized energy and fuel-use records from more than 100 municipal properties into a set of findings the Mayor and Borough Council could act on.

The Problem

The Borough had energy and fuel-usage data across its municipal properties — town hall, schools, public works buildings, and more — but it had never been standardized. Different properties tracked usage differently, some records were incomplete, and none of it was in a form that made it possible to compare properties or spot a trend.

My Role

There was no existing process for this, so I taught myself how to clean, organize, and analyze the dataset — settling on a standard format, reconciling inconsistent records, and building the analysis myself before presenting it alongside the rest of the committee.

Process

I standardized every property's records into a common format, then looked across properties for consumption trends and clear efficiency opportunities rather than just reporting raw totals.

What I Built

• A standardized format for energy and fuel-usage data across 100+ properties
• A trend analysis identifying consumption patterns and efficiency opportunities
• A presentation translating the analysis into cost-conscious recommendations

Outcome

Presented the findings and recommendations directly to the Mayor and Borough Council — putting this kind of cross-property analysis in front of them in a standardized form for the first time.

Reflection

Overview

Through TKS's Innovate Program, I built an AI model from scratch to detect deepfakes — media manipulated using machine learning — and used the project to go deep on adversarial ML, the same category of technique used to both generate and catch this kind of manipulation.

The Problem

Deepfakes are getting harder to catch by eye, and the techniques used to generate them and the techniques used to detect them are locked in the same arms race — as generation improves, detection has to keep up. I wanted to understand that relationship from the inside rather than just reading about it.

My Role

This was an independent build. I researched the underlying adversarial ML techniques myself and built the detection model from scratch, rather than starting from an existing library or pretrained detector.

Process

The model takes in media samples, extracts features associated with manipulation artifacts, and classifies each sample as authentic or manipulated. Researching how generation techniques work was necessary to understand what a detector should actually be looking for.

What I Built

• A trained deepfake-detection model
• Independent research into the adversarial machine-learning techniques used in both generating and detecting manipulated media

Recognition

Separately, as part of a 4-person team in the same TKS program, I worked on the Google x TKS — 2023 Fall Sprint Challenge, recommending "Google See": an internal platform concept to close product-knowledge gaps between Google's sales and engineering teams. Two slides from the recommendation deck we submitted:

From our submitted recommendation deck, with Muhammad Hunain, Ibrahim Rogers, and Natan Kramskiy.

Outcome

Ended the program with a working deepfake-detection model and a much deeper understanding of adversarial ML than I had going in — the kind that comes from actually building the thing, not just reading about it.

Reflection