ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. - Fred Hohman; Nathan O. Hodas; Duen Horng Chau (CHI Extended Abstracts, 2017)

A Deep Learning Approach for Population Estimation from Satellite Imagery. - Caleb Robinson; Fred Hohman; Bistra Dilkina (GeoHumanities@SIGSPATIAL, 2017)

mHealth visual discovery dashboard. - Dezhi Fang; Fred Hohman; Peter J. Polack Jr.; Hillol Sarker; Minsuk Kahng; Moushumi Sharmin; Mustafa al'Absi; Duen Horng Chau (UbiComp/ISWC Adjunct, 2017)

Visual Graph Query Construction and Refinement. - Robert S. Pienta; Fred Hohman; Acar Tamersoy; Alex Endert; Shamkant B. Navathe; Hanghang Tong; Duen Horng Chau (SIGMOD Conference, 2017)

Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression. - Nilaksh Das; Madhuri Shanbhogue; Shang-Tse Chen; Fred Hohman; Li Chen; Michael E. Kounavis; Duen Horng Chau (CoRR, 2017)

Scalable K-Core Decomposition for Static Graphs Using a Dynamic Graph Data Structure. - Alok Tripathy; Fred Hohman; Duen Horng Chau; Oded Green (IEEE BigData, 2018)

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression. - Nilaksh Das; Madhuri Shanbhogue; Shang-Tse Chen; Fred Hohman; Siwei Li; Li Chen; Michael E. Kounavis; Duen Horng Chau (KDD, 2018)

Interactive Classification for Deep Learning Interpretation. - Ángel Alexander Cabrera; Fred Hohman; Jason Lin; Duen Horng Chau (CoRR, 2018)

Large Graph Exploration via Subgraph Discovery and Decomposition. - James Abello; Fred Hohman; Varun Bezzam; Duen Horng Chau (CoRR, 2018)

VIGOR: Interactive Visual Exploration of Graph Query Results. - Robert S. Pienta; Fred Hohman; Alex Endert; Acar Tamersoy; Kevin A. Roundy; Christopher S. Gates; Shamkant B. Navathe; Duen Horng Chau (IEEE Trans. Vis. Comput. Graph., 2018)

Managing Messes in Computational Notebooks. - Andrew Head; Fred Hohman; Titus Barik; Steven Mark Drucker; Robert DeLine (CHI, 2019)

Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. - Fred Hohman; Andrew Head; Rich Caruana; Robert DeLine; Steven Mark Drucker (CHI, 2019)

FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. - Ángel Alexander Cabrera; Will Epperson; Fred Hohman; Minsuk Kahng; Jamie Morgenstern; Duen Horng Chau (VAST, 2019)

Atlas: local graph exploration in a global context. - James Abello; Fred Hohman; Varun Bezzam; Duen Horng Chau (IUI, 2019)

TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning. - Fred Hohman; Arjun Srinivasan; Steven Mark Drucker (IEEE VIS (Short Papers), 2019)

ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features. - Xiangyun Lei; Fred Hohman; Duen Horng (Polo) Chau; Andrew J. Medford (IEEE VIS (Short Papers), 2019)

NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions. - Haekyu Park; Fred Hohman; Duen Horng Chau (CoRR, 2019)

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers. - Fred Hohman; Minsuk Kahng; Robert S. Pienta; Duen Horng Chau (IEEE Trans. Vis. Comput. Graph., 2019)

Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning. - Nilaksh Das; Haekyu Park; Zijie J. Wang; Fred Hohman; Robert Firstman; Emily Rogers; Duen Horng Chau (CHI Extended Abstracts, 2020)

Understanding and Visualizing Data Iteration in Machine Learning. - Fred Hohman; Kanit Wongsuphasawat; Mary Beth Kery; Kayur Patel (CHI, 2020)

The Future of Notebook Programming Is Fluid. - Mary Beth Kery; Donghao Ren; Kanit Wongsuphasawat; Fred Hohman; Kayur Patel (CHI Extended Abstracts, 2020)

CNN 101: Interactive Visual Learning for Convolutional Neural Networks. - Zijie J. Wang; Robert Turko; Omar Shaikh; Haekyu Park; Nilaksh Das; Fred Hohman; Minsuk Kahng; Duen Horng Chau (CHI Extended Abstracts, 2020)

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks. - Mary Beth Kery; Donghao Ren; Fred Hohman; Dominik Moritz; Kanit Wongsuphasawat; Kayur Patel (UIST, 2020)

Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks. - Nilaksh Das; Haekyu Park; Zijie J. Wang; Fred Hohman; Robert Firstman; Emily Rogers; Duen Horng (Polo) Chau (IEEE VIS (Short Papers), 2020)

Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations. - Fred Hohman; Haekyu Park; Caleb Robinson; Duen Horng (Polo) Chau (IEEE Trans. Vis. Comput. Graph., 2020)

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. - Zijie J. Wang; Robert Turko; Omar Shaikh; Haekyu Park; Nilaksh Das; Fred Hohman; Minsuk Kahng; Duen Horng (Polo) Chau (IEEE Trans. Vis. Comput. Graph., 2021)

Interactive Scalable Interfaces for Machine Learning Interpretability. - Frederick Hohman (2021)

Symphony: Composing Interactive Interfaces for Machine Learning. - Alex Bäuerle; Ángel Alexander Cabrera; Fred Hohman; Megan Maher; David Koski; Xavier Suau; Titus Barik; Dominik Moritz (CHI, 2022)

Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. - Jochen Görtler; Fred Hohman; Dominik Moritz; Kanit Wongsuphasawat; Donghao Ren; Rahul Nair; Marc Kirchner; Kayur Patel (CHI, 2022)

NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks. - Haekyu Park; Nilaksh Das; Rahul Duggal; Austin P. Wright; Omar Shaikh; Fred Hohman; Duen Horng (Polo) Chau (IEEE Trans. Vis. Comput. Graph., 2022)

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings. - Zijie J. Wang; Fred Hohman; Duen Horng Chau (ACL (demo), 2023)

Collaborative Machine Learning Model Building with Families Using Co-ML. - Tiffany Tseng; Jennifer King Chen; Mona Abdelrahman; Mary Beth Kery; Fred Hohman; Adriana Hilliard; R. Benjamin Shapiro (IDC, 2023)

Angler: Helping Machine Translation Practitioners Prioritize Model Improvements. - Samantha Robertson; Zijie J. Wang; Dominik Moritz; Mary Beth Kery; Fred Hohman (CHI, 2023)

Designing Data: Proactive Data Collection and Iteration for Machine Learning. - Aspen K. Hopkins; Fred Hohman; Luca Zappella; Xavier Suau Cuadros; Dominik Moritz (CoRR, 2023)

Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences. - Fred Hohman; Mary Beth Kery; Donghao Ren; Dominik Moritz (CHI, 2024)

Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. - Fred Hohman; Chaoqun Wang 002; Jinmook Lee; Jochen Görtler; Dominik Moritz; Jeffrey P. Bigham; Zhile Ren; Cecile Foret; Qi Shan; Xiaoyi Zhang 006 (CHI, 2024)

BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks. - Ruijia Cheng; Titus Barik; Alan Leung; Fred Hohman; Jeffrey Nichols 001 (VL/HCC, 2024)

Apple Intelligence Foundation Language Models. - Tom Gunter; Zirui Wang; Chong Wang; Ruoming Pang; Andy Narayanan; Aonan Zhang; Bowen Zhang 002; Chen Chen 005; Chung-Cheng Chiu; David Qiu; Deepak Gopinath; Dian Ang Yap; et al. (CoRR, 2024)

AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking. - Michelle S. Lam; Fred Hohman; Dominik Moritz; Jeffrey P. Bigham; Kenneth Holstein; Mary Beth Kery (CoRR, 2024)

Exploring Empty Spaces: Human-in-the-Loop Data Augmentation. - Catherine Yeh; Donghao Ren; Yannick Assogba; Dominik Moritz; Fred Hohman (CHI, 2025)

Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments. - Angie Boggust; Venkatesh Sivaraman; Yannick Assogba; Donghao Ren; Dominik Moritz; Fred Hohman (IEEE Trans. Vis. Comput. Graph., 2025)

A Scalable Approach to Clustering Embedding Projections. - Donghao Ren; Fred Hohman; Dominik Moritz (CoRR, 2025)

Embedding Atlas: Low-Friction, Interactive Embedding Visualization. - Donghao Ren; Fred Hohman; Halden Lin; Dominik Moritz (CoRR, 2025)