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ORCIDFred Hohman
Person information
- affiliation: Apple Inc, Seattle, WA, USA
- affiliation (former): Georgia Institute of Technology, Atlanta, GA, USA
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2020 – today
- 2026
[c29]Alex Oesterling
, Donghao Ren, Yannick Assogba, Dominik Moritz, Sunnie S. Y. Kim, Leon A. Gatys, Fred Hohman:
Understanding Annotator Safety Policy with Interpretability. FAccT 2026: 5653-5690- [i32]Alex Oesterling, Donghao Ren, Yannick Assogba, Dominik Moritz, Sunnie S. Y. Kim, Leon A. Gatys, Fred Hohman:
Understanding Annotator Safety Policy with Interpretability. CoRR abs/2605.05329 (2026) - 2025
- [j6]Angie W. Boggust, Venkatesh Sivaraman, Yannick Assogba, Donghao Ren, Dominik Moritz, Fred Hohman:
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments. IEEE Trans. Vis. Comput. Graph. 31(1): 809-819 (2025) - [c28]Catherine Yeh, Donghao Ren, Yannick Assogba, Dominik Moritz, Fred Hohman:
Exploring Empty Spaces: Human-in-the-Loop Data Augmentation. CHI 2025: 847:1-847:19 - [c27]Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery:
Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors. UIST 2025: 178:1-178:24 - [i31]Donghao Ren, Fred Hohman, Dominik Moritz:
A Scalable Approach to Clustering Embedding Projections. CoRR abs/2504.07285 (2025) - [i30]Donghao Ren, Fred Hohman, Halden Lin, Dominik Moritz:
Embedding Atlas: Low-Friction, Interactive Embedding Visualization. CoRR abs/2505.06386 (2025) - [i29]Angie W. Boggust, Donghao Ren, Yannick Assogba, Dominik Moritz, Arvind Satyanarayan, Fred Hohman:
Semantic Regexes: Auto-Interpreting LLM Features with a Structured Language. CoRR abs/2510.06378 (2025) - 2024
- [c26]Fred Hohman, Mary Beth Kery, Donghao Ren, Dominik Moritz:
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences. CHI 2024: 645:1-645:18 - [c25]Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang:
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. CHI 2024: 648:1-648:19 - [c24]Ruijia Cheng, Titus Barik, Alan Leung, Fred Hohman, Jeffrey Nichols:
BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks. VL/HCC 2024: 13-23 - [i28]Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang:
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. CoRR abs/2404.03085 (2024) - [i27]Ruijia Cheng, Titus Barik, Alan Leung, Fred Hohman, Jeffrey Nichols:
BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks. CoRR abs/2404.07387 (2024) - [i26]Angie W. Boggust, Venkatesh Sivaraman, Yannick Assogba, Donghao Ren, Dominik Moritz, Fred Hohman:
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments. CoRR abs/2408.03274 (2024) - [i25]Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery:
AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking. CoRR abs/2409.18203 (2024) - [i24]Catherine Yeh, Donghao Ren, Yannick Assogba, Dominik Moritz, Fred Hohman:
Exploring Empty Spaces: Human-in-the-Loop Data Augmentation. CoRR abs/2410.01088 (2024) - 2023
- [c23]Zijie J. Wang, Fred Hohman, Duen Horng Chau:
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings. ACL (demo) 2023: 516-523 - [c22]Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery, Fred Hohman, Adriana Hilliard, R. Benjamin Shapiro:
Collaborative Machine Learning Model Building with Families Using Co-ML. IDC 2023: 40-51 - [c21]Samantha Robertson, Zijie J. Wang, Dominik Moritz, Mary Beth Kery, Fred Hohman:
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements. CHI 2023: 832:1-832:20 - [i23]Aspen K. Hopkins, Fred Hohman, Luca Zappella, Xavier Suau Cuadros, Dominik Moritz:
Designing Data: Proactive Data Collection and Iteration for Machine Learning. CoRR abs/2301.10319 (2023) - [i22]Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery, Fred Hohman, Adriana Hilliard, R. Benjamin Shapiro:
Collaborative Machine Learning Model Building with Families Using Co-ML. CoRR abs/2304.05444 (2023) - [i21]Samantha Robertson, Zijie J. Wang, Dominik Moritz, Mary Beth Kery, Fred Hohman:
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements. CoRR abs/2304.05967 (2023) - [i20]Zijie J. Wang, Fred Hohman, Duen Horng Chau:
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings. CoRR abs/2306.09328 (2023) - [i19]Fred Hohman, Mary Beth Kery, Donghao Ren, Dominik Moritz:
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences. CoRR abs/2310.04621 (2023) - 2022
- [j5]Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P. Wright, Omar Shaikh, Fred Hohman, Duen Horng (Polo) Chau:
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks. IEEE Trans. Vis. Comput. Graph. 28(1): 813-823 (2022) - [c20]Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz:
Symphony: Composing Interactive Interfaces for Machine Learning. CHI 2022: 210:1-210:14 - [c19]Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel:
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. CHI 2022: 408:1-408:13 - [i18]Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz:
Symphony: Composing Interactive Interfaces for Machine Learning. CoRR abs/2202.08946 (2022) - 2021
- [b1]Frederick Hohman:
Interactive Scalable Interfaces for Machine Learning Interpretability. Georgia Institute of Technology, Atlanta, GA, USA, 2021 - [j4]Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng (Polo) Chau:
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Trans. Vis. Comput. Graph. 27(2): 1396-1406 (2021) - [i17]Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P. Wright, Omar Shaikh, Fred Hohman, Duen Horng Chau:
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks. CoRR abs/2108.12931 (2021) - [i16]Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel:
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. CoRR abs/2110.12536 (2021) - 2020
- [j3]Fred Hohman, Haekyu Park, Caleb Robinson, Duen Horng (Polo) Chau:
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations. IEEE Trans. Vis. Comput. Graph. 26(1): 1096-1106 (2020) - [c18]Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau:
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning. CHI Extended Abstracts 2020: 1-7 - [c17]Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, Kayur Patel:
Understanding and Visualizing Data Iteration in Machine Learning. CHI 2020: 1-13 - [c16]Mary Beth Kery, Donghao Ren, Kanit Wongsuphasawat, Fred Hohman, Kayur Patel:
The Future of Notebook Programming Is Fluid. CHI Extended Abstracts 2020: 1-8 - [c15]Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau:
CNN 101: Interactive Visual Learning for Convolutional Neural Networks. CHI Extended Abstracts 2020: 1-7 - [c14]Mary Beth Kery, Donghao Ren, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Kayur Patel:
mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks. UIST 2020: 140-151 - [c13]Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng (Polo) Chau:
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks. IEEE VIS (Short Papers) 2020: 271-275 - [i15]Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau:
CNN 101: Interactive Visual Learning for Convolutional Neural Networks. CoRR abs/2001.02004 (2020) - [i14]Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau:
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning. CoRR abs/2001.07769 (2020) - [i13]Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau:
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. CoRR abs/2004.15004 (2020) - [i12]Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau:
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks. CoRR abs/2009.02608 (2020) - [i11]Mary Beth Kery, Donghao Ren, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Kayur Patel:
mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks. CoRR abs/2009.10643 (2020)
2010 – 2019
- 2019
- [j2]Fred Hohman, Minsuk Kahng, Robert S. Pienta, Duen Horng Chau:
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers. IEEE Trans. Vis. Comput. Graph. 25(8): 2674-2693 (2019) - [c12]Andrew Head, Fred Hohman, Titus Barik, Steven Mark Drucker, Robert DeLine:
Managing Messes in Computational Notebooks. CHI 2019: 270 - [c11]Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, Steven Mark Drucker:
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. CHI 2019: 579 - [c10]Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau:
FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. VAST 2019: 46-56 - [c9]James Abello, Fred Hohman, Varun Bezzam, Duen Horng Chau:
Atlas: local graph exploration in a global context. IUI 2019: 165-176 - [c8]Fred Hohman, Arjun Srinivasan, Steven Mark Drucker:
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning. IEEE VIS (Short Papers) 2019: 151-155 - [c7]Xiangyun Lei, Fred Hohman, Duen Horng (Polo) Chau, Andrew J. Medford:
ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features. IEEE VIS (Short Papers) 2019: 196-200 - [i10]Fred Hohman, Haekyu Park, Caleb Robinson, Duen Horng Chau:
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations. CoRR abs/1904.02323 (2019) - [i9]Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau:
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning. CoRR abs/1904.05419 (2019) - [i8]Haekyu Park, Fred Hohman, Duen Horng Chau:
NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions. CoRR abs/1906.00332 (2019) - [i7]Xiangyun Lei, Fred Hohman, Duen Horng Chau, Andrew J. Medford:
ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features. CoRR abs/1908.08381 (2019) - 2018
- [j1]Robert S. Pienta, Fred Hohman, Alex Endert, Acar Tamersoy, Kevin A. Roundy, Christopher S. Gates, Shamkant B. Navathe, Duen Horng Chau:
VIGOR: Interactive Visual Exploration of Graph Query Results. IEEE Trans. Vis. Comput. Graph. 24(1): 215-225 (2018) - [c6]Alok Tripathy, Fred Hohman, Duen Horng Chau, Oded Green:
Scalable K-Core Decomposition for Static Graphs Using a Dynamic Graph Data Structure. IEEE BigData 2018: 1134-1141 - [c5]Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau:
SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression. KDD 2018: 196-204 - [i6]Fred Hohman, Minsuk Kahng, Robert S. Pienta, Duen Horng Chau:
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers. CoRR abs/1801.06889 (2018) - [i5]Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau:
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression. CoRR abs/1802.06816 (2018) - [i4]Ángel Alexander Cabrera, Fred Hohman, Jason Lin, Duen Horng Chau:
Interactive Classification for Deep Learning Interpretation. CoRR abs/1806.05660 (2018) - [i3]James Abello, Fred Hohman, Varun Bezzam, Duen Horng Chau:
Large Graph Exploration via Subgraph Discovery and Decomposition. CoRR abs/1808.04414 (2018) - 2017
- [c4]Fred Hohman, Nathan O. Hodas, Duen Horng Chau:
ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. CHI Extended Abstracts 2017: 1694-1699 - [c3]Caleb Robinson, Fred Hohman, Bistra Dilkina:
A Deep Learning Approach for Population Estimation from Satellite Imagery. GeoHumanities@SIGSPATIAL 2017: 47-54 - [c2]Dezhi Fang, Fred Hohman, Peter J. Polack Jr., Hillol Sarker, Minsuk Kahng, Moushumi Sharmin, Mustafa al'Absi, Duen Horng Chau:
mHealth visual discovery dashboard. UbiComp/ISWC Adjunct 2017: 237-240 - [c1]Robert S. Pienta, Fred Hohman, Acar Tamersoy, Alex Endert, Shamkant B. Navathe, Hanghang Tong, Duen Horng Chau:
Visual Graph Query Construction and Refinement. SIGMOD Conference 2017: 1587-1590 - [i2]Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E. Kounavis, Duen Horng Chau:
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression. CoRR abs/1705.02900 (2017) - [i1]Caleb Robinson, Fred Hohman, Bistra Dilkina:
A Deep Learning Approach for Population Estimation from Satellite Imagery. CoRR abs/1708.09086 (2017)
Coauthor Index
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