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.

Exploring Empty Spaces
Human-in-the-loop data augmentation
CHI 2025
Project PDF Code
Model Compression in Practice
Lessons learned from practitioners creating on-device machine learning experiences
CHI 2024
Project PDF Video Preview
Talaria
Interactively optimizing machine learning models for efficient inference
CHI 2024
Project PDF Video Preview


Apple Human Interface Guidelines

Guidance and best practices to help designers and developers create great experience for any Apple platform.

Generative AI
Generative AI empowers you to enhance your app or game with dynamic content and offer intelligent features that unlock new levels of creativity, connection, and productivity
2025
Project
Charting Data
Presenting data in a chart can help you communicate information with clarity and appeal
2022
Project
Charts
Organize data in a chart to communicate information with clarity and visual appeal
2022
Project


Featured Dissertation Publications

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

Interactive Scalable Interfaces for Machine Learning Interpretability

Fred Hohman

Dissertation, Georgia Tech, 2020.

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
Visual Analytics in Deep Learning
An interrogative survey
TVCG 2018
Project Demo PDF Blog Video Code Slides


Featured Interactive Articles

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

Machine Learning Research Articles
Interactively explore advancements in machine learning research at Apple
May 2021 — Present
Project
Menstrual Cycle Length and Variability: A Visual Explanation
Visualizing the Apple Women's Health Study
2023
Project
Communicating with Interactive Articles
Examining interactive article design by synthesizing theory from education, journalism, and visualization
Distill 2020
Project Demo PDF Code


Parametric Press

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

Parametric Press: Issue 02
Interactively analyze and learn about our climate’s past, present, and future
2020
Project Code
Parametric Press: Issue 01
Examine scientific and technological phenomena that stand to shape society at large
2019
Project Code