Hi, I'm Fred Hohman

I'm a Research Scientist at Apple
I design and develop interactive interfaces to help people understand machine learning models and data-driven systems. Besides building tools, I also create data visualizations and write interactive articles to simply communicate complex ideas.
I received my PhD from Georgia Tech where I worked with Polo Chau and Alex Endert. My dissertation on interactive interfaces for interpretability won the ACM SIGCHI Outstanding Dissertation Award and was supported by a NASA Space Technology Research Fellowship.
I have collaborated with designers, developers, artists, and scientists while working at Apple, Microsoft Research, NASA Jet Propulsion Lab, and Pacific Northwest National Lab.

Featured Research Publications

Latest research for fans of human-computer interaction, data visualization, and machine learning.

Lessons learned from practitioners creating on-device machine learning experiences
CHI 2024
Interactively optimizing machine learning models for efficient inference
CHI 2024
Generalizing confusion matrix visualization to hierarchical and multi-output labels
CHI 2022

Featured Dissertation Publications

My dissertation contributed interactive interfaces to enable machine learning interpretability at scale and for everyone.

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

Apple Chart Design Guidelines

Guidance and best practices to help designers and developers create the best charts for Apple platforms.

Presenting data in a chart can help you communicate information with clarity and appeal
Apple Human Interface Guidelines 2022
A chart helps you communicate data in a graphical, approachable way
Apple Human Interface Guidelines 2022

Featured Interactive Articles

Enhanced reading experiences that demonstrate what's possible when dynamic media are effectively combined.

Interactively explore advancements in machine learning research at Apple
May 2021 — Present
Visualizing the Apple Women's Health Study
Examining interactive article design by synthesizing theory from education, journalism, and visualization
Distill 2020

Parametric Press

A born-digital, experimental magazine dedicated to showcasing the expository power of the web.

Interactively analyze and learn about our climate’s past, present, and future
Examine scientific and technological phenomena that stand to shape society at large