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Hi 👋, I'm Olivier

I'm a PhD statistician, data scientist, and engineering enthusiast.

🤓 Some stats

🧑‍💻 Some of the software I've worked on

  • ER-Evaluation (Python package on PyPI): Toolkit for the (statistical) evaluation of entity resolution, record linkage, and micro-clustering models.

  • StreamlitSurvey (Python package on PyPI): Streamlit apps can be a bit tricky to get right. Especially if you want multi-pages forms/surveys that keep track of progress and that can be restored from a saved state. This Streamlit component makes it easy to incorporate more complex forms on in your apps.

  • StringCompare (Python/C++ package): Most implementations of the Levenshtein distance are pretty bad. Here's how the basic algorithm can be implemented with minimal time and memory complexity.

  • CSVMeta (Python package on PyPI): You know how excel has been repeatedly blamed for high-profile disasters? Well, CSV files are part of the problem. If you're going to use CSV files, you need to specify your CSV dialect in a metadata file. This package reads/write CSV files with attached dialect information.

  • assert (R package on CRAN): Informative assertions in 16 lines of code with some R introspection magic (no dependencies!)

  • cache (R package on CRAN): On-disk caching for data science workflows in 15 lines of code (no dependencies!)

  • dgaFast/dga (R package on CRAN): I needed the dga R package (that I know maintain) to go fast. I re-implemented bottlenecks in C++ and did some linear algebra magic for a 100x speedup.

  • Fractals (Javascript App): More than 10 years ago, I deep dived into browser multithreading to create a Javascript Mendelbrot set visualization app.

  • Welcome to the Moon App (Javascript App): I love board games! I created this app as a companion to the wonderful Welcome to the Moon board game.

  • FingermatchR (R & C package): Ever needed an R wrapper to fingerprint matching C libraries? No? Well, a Duke postdoc did and sweet-talked me into building this... It may not be the most useful thing, but it was interesting!

🧰 Some tools I use

Python R C C++ Java bash JavaScript DuckDB AWS Docker ElasticSearch Git Github Actions conda Linux vscode

NumPy Pandas Plotly scikit-learn PyTorch statsmodels

Pytest Pytest

Olivier Binette's Projects

al-folio icon al-folio

A beautiful, simple, clean, and responsive Jekyll theme for academics

assert icon assert

Lightweight validation tool for checking function arguments and data analysis scripts.

bayesclassifier icon bayesclassifier

MCMC simulation algorithm for bayesian binary classification. Code associated to a blog post.

boom icon boom

A C++ library for Bayesian modeling, mainly through Markov chain Monte Carlo, but with a few other methods supported. BOOM = "Bayesian Object Oriented Modeling". It is also the sound your computer makes when it crashes.

c-stringcompare icon c-stringcompare

C++ library for efficient string similarity computation and approximate string matching. Backend to the stringcompare Python package.

cache icon cache

Easily cache and retrieve computation results in R

clevr icon clevr

Clustering and Link Prediction Evaluation in R

cora icon cora

Cora data set for Entity Resolution

csv-search icon csv-search

Quickly setup elasticsearch and a web search UI for arbitrary csv tables.

csvmeta icon csvmeta

Lightweight csv read/write, keeping track of csv dialect and other metadata.

deepchecks icon deepchecks

Tests for Continuous Validation of ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.

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