Hi, I'm Fred Hohman

I’m a PhD candidate in the College of Computing at Georgia Tech.
I research how to enable machine learning interpretability at scale and for everyone, by designing and developing interactive interfaces to help people confidently understand data-driven systems. Besides building tools, I also create data visualizations and write interactive articles to simply communicate complex ideas.
At Georgia Tech I work with Polo Chau and Alex Endert as a member of the Polo Club of Data Science.
I have collaborated with designers, developers, and scientists while working at Apple, Microsoft Research, NASA Jet Propulsion Lab, and Pacific Northwest National Lab.
My research is supported by a NASA Space Technology Research Fellowship.

Featured Research Publications

Communicating with Interactive Articles
Examining interactive article design by synthesizing theory from education, journalism, and visualization
Distill 2020
Project Demo PDF Code
Chameleon
Understanding and visualizing data iteration in machine learning
CHI 2020
Project PDF Video Recording Preview Slides
Summit
Scaling deep learning interpretability by visualizing activation and attribution summarizations
TVCG 2020
Project Demo PDF Video Recording Code Slides
Gamut
A design probe to understand how data scientists understand machine learning models
CHI 2019
Project Demo PDF Blog Video Preview Slides

Featured Interactive Articles

Parametric Press
A born-digital magazine showcasing the expository power of the web
Present —
Project Code
The Myth of the Impartial Machine
Can we free machine learning models from bias and prejudice?
2019
Project Code
Dimensionality Reduction
Explore the methods data scientists use to visualize high-dimensional data
2018
Project Code Slides
The Math of Card Shuffling
Riffling from factory order to complete randomness
2018
Project Code

Everything Else

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