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Hi There!

TL;DR: I am a Ph.D. student studying the interpretability in deep learning looking for industrial research positions

Take a look at my github.io to see my past publications and blog.

  • Pronouns: he/him
  • Twitter: @ihowrue
  • Currently learning about: generative models and what makes an interpretability method useful

Recently pivoted my research towards interpretability in deep learning.

In the past, I have worked on the use of visualization of backtrack search when solving a constraint satisfaction problem. For my undergraduaate thesis, I developed an educational and research tool visualizing how Sudoku can be solved using the Constraint Satisfaction Problem framework, and how this framework is beneficial. This tool can be found at https://sudoku.unl.edu

I enjoy working with

Deep learning, Python, C++, JavaScript (and its frameworks), Optimization

I'm looking to collaborate on

Anything! I have an unconventional background for working in deep learning, and I'm interested in the overlap between my past and current work.

Ian Howell's Projects

azusa icon azusa

An 'on curve' estimator for given Magic: the Gathering decks.

baselines icon baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

cppflow icon cppflow

Run TensorFlow models in C++ without installation and without Bazel

metrics icon metrics

Machine learning metrics for distributed, scalable PyTorch applications.

mpc-autofill icon mpc-autofill

Automating MakePlayingCards's online ordering system for Magic: The Gathering casual proxies

mtgspykit icon mtgspykit

A repository of information for mtg deck name origins.

r2048-rl icon r2048-rl

A tensor implementation of the 2048 game.

tcg-to-deckbox icon tcg-to-deckbox

Python script to convert TCGPlayer app's export file into the proper format for deckbox.org's import

unluigi icon unluigi

Starter code for running luigi on UNL's HCC cluster.

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