Fred Hohman

schoolGeorgia Institute of Technologybar_chart#2334 Global Rank

Total Papers

HCI Rank

#2334

Research Areas

Years Active

Recent Publications

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks

We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this flexibility, we extend computational notebooks with a new API mage, which supports tools that can represent…

CHI '201st AuthorInteractive Data Visualization +1

Understanding and Visualizing Data Iteration in Machine Learning

Successful machine learning (ML) applications require iterations on both modeling and the underlying data. While prior visualization tools for ML primarily focus on modeling, our interviews with 23 ML practitioners reveal that they improve model performance frequently by iterating on their data (e.g., collecting new d…

CHI '192nd AuthorBest PaperInteractive Data Visualization +1

Managing Messes in Computational Notebooks

Data analysts use computational notebooks to write code for analyzing and visualizing data. Notebooks help analysts iteratively write analysis code by letting them interleave code with output, and selectively execute cells. However, as analysis progresses, analysts leave behind old code and outputs, and overwrite impo…

IUI '192nd AuthorInteractive Data Visualization +1

Atlas: Local Graph Exploration in a Global Context

Graphs are everywhere, growing increasingly complex, and still lack scalable, interactive tools to support sensemaking. To address this problem, we present Atlas, an interactive graph exploration system that adapts scalable edge decomposition to enable a new paradigm for large graph exploration, generating explorable…

CHI '191st AuthorExplainable AI (XAI) +2

Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models

Without good models and the right tools to interpret them, data scientists risk making decisions based on hidden biases, spurious correlations, and false generalizations. This has led to a rallying cry for model interpretability. Yet the concept of interpretability remains nebulous, such that researchers and tool desi…

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