Hi, I'm Fred Hohman

Iā€™m a Ph.D. candidate in the College of Computing at Georgia Tech advised by Polo Chau and Alex Endert.

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.

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

Understanding and visualizing data iteration in machine learning
CHI 2020
Scaling deep learning interpretability by visualizing activation and attribution summarizations
TVCG 2020
A design probe to understand how data scientists understand machine learning models
CHI 2019

Featured Interactive Articles

A born-digital magazine showcasing the expository power of the web
Present ā€”
Can we free machine learning models from bias and prejudice?
May 2019
Explore the methods data scientists use to visualize high-dimensional data
July 2018
Riffling from factory order to complete randomness
June 2018

Everything Else

Including a list of projects, the blog, monthly music playlists, stuff I use, and the archive.