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ImaginationVellum: Generative-AI Ideation Canvas with Spatial Prompts, Generative Strokes, and Ideation History

We introduce ImaginationVellum, a multi-modal spatial canvas for early-stage visual ideation and concept sketching with generative AI. The resulting system supports a unique style of human-AI co-creation where the canvas is the prompt. This means that ImaginationVellum employs the entire 2D canvas as an active prompt…

Nicolai Marquardt et al.Microsoft

Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way

Data analysts often need to iterate between data transformations and chart designs to create rich visualizations for exploratory data analysis. Although many AI-powered systems have been introduced to reduce the effort of visualization authoring, existing systems are not well suited for iterative authoring. They typic…

Chenglong Wang et al.Microsoft

How Do Analysts Understand and Verify AI-Assisted Data Analyses?

Data analysis is challenging as it requires synthesizing domain knowledge, statistical expertise, and programming skills. Assistants powered by large language models (LLMs), such as ChatGPT, can assist analysts by translating natural language instructions into code. However, AI-assistant responses and analysis code ca…

Ken Gu et al.University Of Washington

On the Design of AI-powered Code Assistants for Notebooks

AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component of contemporary coding contexts. Among these environments, computational notebooks, such as Jupyter, are of particular interest as they provide rich interface affordances that interleave code and output in a manner that allows for…

Andrew McNutt et al.University of Chicago

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Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis

Data science is characterized by evolution: since data science is exploratory, results evolve from moment to moment; since it can be collaborative, results evolve as the work changes hands. While existing tools help data scientists track changes in code, they provide less support for understanding the iterative change…

April Yi Wang et al.University of Michigan

Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations

Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a…

Arjun Srinivasan et al.Tableau

Fork It: Supporting Stateful Alternatives in Computational Notebooks

Computational notebooks, which seamlessly interleave code with results, have become a popular tool for data scientists due to the iterative nature of exploratory tasks. However, notebooks provide a single execution state for users to manipulate through creating and manipulating variables. When exploring alternatives,…

Nathaniel Weinman et al.University of California - Berkeley

Affinity Lens: Data-Assisted Affinity Diagramming with Augmented Reality

Despite the availability of software to support Affinity Diagramming (AD), practitioners still largely favor physical sticky-notes. Physical notes are easy to set-up, can be moved around in space and offer flexibility when clustering un-structured data. However, when working with mixed data sources such as surveys, de…

Hariharan Subramonyam et al.University of Michigan

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…

Fred Hohman et al.Georgia Institute of Technology

AnchorViz: Facilitating Classifier Error Discovery through Interactive Semantic Data Exploration

In supervised interactive machine learning, human knowledge about the target concept can be a powerful reference to build a concept classifier that is robust to unseen items in the real world. The main challenge lies in finding unlabeled items that can either help discover or refine subconcepts for which the current c…

Nan-Chen Chen et al.University Of Washington

What's the Difference?: Evaluating Variations of Multi-Series Bar Charts for Visual Comparison Tasks

An increasingly common approach to data analysis involves using information dashboards to visually compare changing data. However, layout constraints coupled with varying levels of visualization literacy among dashboard users make facilitating visual comparison in dashboards a challenging task. In this paper, we evalu…

Arjun Srinivasan et al.Tableau

Paper Title	Authors	Research Topics	Paper Database	Year
emoji_events ImaginationVellum: Generative-AI Ideation Canvas with Spatial Prompts, Generative Strokes, and Ideation History We introduce ImaginationVellum, a multi-modal spatial canvas for early-stage visual ideation and concept sketching with generative AI. The resulting system supports a unique style of human-AI co-creation where the canvas is the prompt. This means that ImaginationVellum employs the entire 2D canvas as an active prompt…	NM Nicolai Marquardt et al.Microsoft
Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way Data analysts often need to iterate between data transformations and chart designs to create rich visualizations for exploratory data analysis. Although many AI-powered systems have been introduced to reduce the effort of visualization authoring, existing systems are not well suited for iterative authoring. They typic…	CW Chenglong Wang et al.Microsoft
How Do Analysts Understand and Verify AI-Assisted Data Analyses? Data analysis is challenging as it requires synthesizing domain knowledge, statistical expertise, and programming skills. Assistants powered by large language models (LLMs), such as ChatGPT, can assist analysts by translating natural language instructions into code. However, AI-assistant responses and analysis code ca…	KG Ken Gu et al.University Of Washington
On the Design of AI-powered Code Assistants for Notebooks AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component of contemporary coding contexts. Among these environments, computational notebooks, such as Jupyter, are of particular interest as they provide rich interface affordances that interleave code and output in a manner that allows for…	AM Andrew McNutt et al.University of Chicago
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis Data science is characterized by evolution: since data science is exploratory, results evolve from moment to moment; since it can be collaborative, results evolve as the work changes hands. While existing tools help data scientists track changes in code, they provide less support for understanding the iterative change…	AW April Yi Wang et al.University of Michigan
Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a…	AS Arjun Srinivasan et al.Tableau
Fork It: Supporting Stateful Alternatives in Computational Notebooks Computational notebooks, which seamlessly interleave code with results, have become a popular tool for data scientists due to the iterative nature of exploratory tasks. However, notebooks provide a single execution state for users to manipulate through creating and manipulating variables. When exploring alternatives,…	NW Nathaniel Weinman et al.University of California - Berkeley
emoji_events Affinity Lens: Data-Assisted Affinity Diagramming with Augmented Reality Despite the availability of software to support Affinity Diagramming (AD), practitioners still largely favor physical sticky-notes. Physical notes are easy to set-up, can be moved around in space and offer flexibility when clustering un-structured data. However, when working with mixed data sources such as surveys, de…	HS Hariharan Subramonyam et al.University of Michigan
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…	FH Fred Hohman et al.Georgia Institute of Technology
AnchorViz: Facilitating Classifier Error Discovery through Interactive Semantic Data Exploration In supervised interactive machine learning, human knowledge about the target concept can be a powerful reference to build a concept classifier that is robust to unseen items in the real world. The main challenge lies in finding unlabeled items that can either help discover or refine subconcepts for which the current c…	NC Nan-Chen Chen et al.University Of Washington
What's the Difference?: Evaluating Variations of Multi-Series Bar Charts for Visual Comparison Tasks An increasingly common approach to data analysis involves using information dashboards to visually compare changing data. However, layout constraints coupled with varying levels of visualization literacy among dashboard users make facilitating visual comparison in dashboards a challenging task. In this paper, we evalu…	AS Arjun Srinivasan et al.Tableau