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Hello! I do work in the areas of machine learning, physics, and software development. I also work on improving methods for testing scientific/research software, and am a maintainer for the Hypothesis testing library. I am passionate about education, and created the CogWorks course at the MIT Beaver Works Summer Institute as well as the website Python Like You Mean It.

Libraries for accelerating and improving ML research

  • hydra-zen: Making Hydra more pythonic and easier to use at-scale for ML workflows and expriments
  • responsible-ai-toolbox: PyTorch-centric library for evaluating and enhancing the robustness of AI technologies.

Other open source projects

  • MyGrad: Drop-in automatic differentiation for NumPy
  • noggin: A simple tool for logging and plotting metrics in real time
  • custom_inherit: inheriting and merging docstrings in customizable ways (my first ever open source project!)

Tutorials

Ryan Soklaski's Projects

18337 icon 18337

18.337 - Parallel Computing and Scientific Machine Learning

18s191 icon 18s191

Course 18.S191 at MIT, fall 2020 - Introduction to computational thinking with Julia:

albumentations icon albumentations

fast image augmentation library and easy to use wrapper around other libraries

cog_datasets icon cog_datasets

Save machine learning data sets to a common location. (Educational resource)

custom_inherit icon custom_inherit

A Python package that provides tools for inheriting docstrings in customizable ways.

flax icon flax

Flax is a neural network library for JAX that is designed for flexibility.

ghosty icon ghosty

Dummy code for testing hypothesis' ghostwriter

graph_strat icon graph_strat

hypothesis-networkx strategy for drawing constrained graphs

introtojulia icon introtojulia

A Deep Introduction to Julia for Data Science and Scientific Computing

mypy icon mypy

Optional static typing for Python

mypy_primer icon mypy_primer

Run mypy and pyright over millions of lines of code

noggin icon noggin

A simple tool for logging and plotting measurements during machine learning experiments

numpy icon numpy

The fundamental package for scientific computing with Python.

omegaconf icon omegaconf

Flexible Python configuration system. The last one you will ever need.

phantom-tensors icon phantom-tensors

Tensor-like types – with variadic shapes – that support both static and runtime type checking, and convenient parsing

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