All work AI & Data · Project 08 / 08

Data Science & Machine Learning

Applied Python, SQL, statistics, machine learning, and analytical methods to real-world datasets and modeling problems — and now to Project Athena, which asks whether creators can become better AI contributors.

Now: Project Athena
Program: Data Science & ML · 10 months
Tools: Python · SQL · scikit-learn
Methods: Regression, classification, EDA

A stack of books on data, probability, and information, including The Black Swan, Big Data, The Information, and Chaos — Fig. 8.0 — The reading list

Now — In progressAI · Human data · Education · Assessment

Project Athena

A project that teaches creators to become better AI contributors.

AI labs increasingly depend on human expertise, as they should. But across training, evaluation, feedback, safety testing, and model improvement, there is still significant room for improvement. Today, human contribution is often treated as a raw data source rather than an intentional curation process.

The research question

Can we teach creators to become better AI contributors?”

Premise

Creators already have the expertise

Creators already possess valuable expertise in areas such as culture, media, design, storytelling, research, language, and consumer behavior — but how can this be applied to building the future of AI?

Culture
Media
Design
Storytelling
Research
Language
Consumer behavior

Athena investigates whether structured education and assessment can help creators translate that expertise into higher-quality AI training and evaluation data.

Measurement

How I’ll know it works

Models are becoming more powerful at a rapid pace, and it’s important to get ahead of potentially harmful outputs by helping models develop safely. I’ll start by recruiting a first cohort and measuring the quality of their contributions before and after they complete an education course.

My goal is to partner with established frontier AI labs and share this approach with the industry in a way that also supports their internal research.

Program

Why I went deeper

I’m completing a 10-month Data Science & Machine Learning program, where I’m developing a stronger foundation in Python, SQL, statistics, machine learning, and data analysis. The program inspired me to pursue this research.

Toolkit

What I work with

Python (pandas, NumPy, scikit-learn, matplotlib/seaborn) · SQL · Statistics · Regression · Classification · Feature selection · Model evaluation · Data visualization.

Find the full code on GitHub.

Next — Project 01 Violet Verse