Uncool
Data Science.

I started this site in 2020 to look past the hype and understand what actually worked. Six years later, with the rise of AI assistants, I’m creating things I wouldn’t have imagined attempting then. Follow what I was questioning then and what we can build now.

Do Androids Dream of Running Out of Claude Opus 5.5 Tokens While Animating Electric Sheep?

Explore the timeline ↓GitHub ↗

2026 / NOW

New adventures in music and machine learning (Op. 55)

AI assistants have changed how quickly we can turn an idea into a working experiment. Some questions remain difficult. Exploring them has become much easier.

Featured project · Live application

CoverDetector↗

Different recording.
Same song?

Recognizing a particular recording is one thing. Recognizing the same composition through a different singer, tempo, key, or arrangement is another. Cover-song identification has made substantial progress, but radically different interpretations and real-world recordings still challenge current systems.

CoverDetector explores that problem using audio embeddings from CoverHunter and LIVI to search for related recordings. Building the application is only part of the experiment: evaluating its matches, understanding its mistakes, and separating musical identity from superficial similarity are where the harder questions begin.

Try it, listen to the results, and see where it falls short.

Audio embeddingsCoverHunter + LIVISimilarity search

Try CoverDetector ↗View code ↗

coverdetector.com

CoverDetector finding possible covers of Pete Seeger’s Joshua Fought the Battle of Jericho

2024 / MUSIC2VEC2024

What does a song look like
to a model?

Can we find relationships between songs by representing them as vectors?

I started with Chromaprint2Vec, using Chromaprint—an audio fingerprinting tool designed to identify recordings—and MusicBrainz, an open music metadata database.

Harmony2Vec explored learned audio embeddings with CoverHunterMPS, an adaptation of the CoverHunter cover-song identification model that runs on a single computer, alongside chord progression tools.

Mapping these representations lets us inspect which recordings end up close together. Some relationships make musical sense to us. Others are harder to interpret. What has the model learned to consider similar?

Songs represented in the embedding projector — Music2vec · exploring the relationships between recordings.

Open the music experiments ↗Explore Chromaprint2Vec ↗Explore Harmony2Vec ↗

2021 / UNCOOL DATA SCIENCE

Assumptions strike back.

Jun 2021

Causal inference · Updated 17 June 2021

Causal wars: assumptions strike back

What can algorithms tell us about cause and effect? Experiments with automatic causal discovery, and the assumptions behind the answers.

Read the original article ↗Explore the notebooks ↗

2020 / THE SITE BEGINS

Look past the hype.

I started this site to question the promises, work through the methods, and make the assumptions explicit. These articles remain in the context in which I wrote them.

Oct 2020

Causal discovery · Updated 22 October 2020

No free lunch in automatic causal discovery

Testing causal discovery methods against examples where the answer seems obvious—and finding that it isn’t so simple.

Read the original article ↗Explore the notebook ↗

Aug 2020

Statistics · 2 August 2020

Regression to linear regression

Revisiting the fundamentals through health expenditure and life expectancy: model assumptions, panel data, and what the residuals reveal.

Scatterplots and distributions of health expenditure and life expectancy — From the original regression experiments.

Read the original article ↗Explore the notebook ↗

May 2020

COVID-19 / Analysis & simulations

Estimates, context, and the language of uncertainty

Notes on test errors, bias, and interpreting pandemic data, preserved in their original context.

Read the original analyses ↗

2019 / BEFORE THIS SITE

Workshops & experiments.

2019

Computer vision / Workshop

Car Behavioral Cloning

“Agárrate que vienen curvas.” Workshop materials comparing deep learning and classical machine learning in a self-driving car simulator, based on the Udacity project and NVIDIA’s convolutional model.