About

Jeffrey Michael Heer is the Jerre D. Noe Endowed Professor of Computer Science & Engineering at the University of Washington. He leads the UW Interactive Data Lab and is deeply engaged in teaching data visualization courses, including CSE442 and CSE512. Professor Heer values working with talented and engaging students, mentoring a diverse group of PhD students, post-doctoral scholars, masters, and undergraduate students in areas related to data visualization, interactive machine learning, statistical analysis, and computational visualization interpretation. His research interests encompass authoring interactive documents, visual debugging tools, visualization perception, models and automated design, interactive machine learning, error analysis, and languages and tools for interactive visualization. Through his mentorship, many of his former students and post-doctoral scholars have gone on to prominent roles in academia and industry, contributing to fields such as scalable interactive visualization, uncertainty visualization, genomic data analysis, and visualization recommendation systems.

Research topics

• Computer Science
• Software engineering
• Epistemology
• Natural Language Processing
• Artificial Intelligence
• Data science
• Programming language
• Econometrics
• Mathematics
• Psychology
• Philosophy
• Linguistics
• Social psychology
• Statistics
• Theoretical computer science
• Engineering

Selected publications

• GeoVisA11y: An AI-based Geovisualization Question-Answering System for Screen-Reader Users

  2026-04-13 · 1 citations

  articleOpen access

  Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual, and contextual queries. Through user studies with six screen-reader users and six sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.

  PublisherDOI

• RDoFlow: Automatically assessing under-specified statistical analyses in HCI

  2026-03-03 · 1 citations

  articleOpen accessSenior author

  When designing and analyzing a study, researchers must navigate a large space of methodological decisions, or “researcher degrees of freedom.” If these choices are not preregistered or transparently reported, they can increase the risk of inflated false-positive rates and exaggerated effect sizes, undermining scientific credibility. Drawing on psychology research that characterizes these degrees of freedom, we create a protocol for scoring how hypotheses are reported in the HCI literature (ReportDoF). We manually apply ReportDoF to 100 hypotheses from HCI texts authored between 2015-2025, including both preregistrations and papers. Based on this experience, we contribute an LLM workflow and proof-of-concept interactive interface (RDoFlow) that applies ReportDoF to new texts, enabling large-scale analysis of the composition and quality of reported analysis specifications. For example, RDoFlow reveals that HCI research more frequently tests multiple dependent variables for a single hypothesis than psychology research does—a practice that increases the risk of false positives.

  PublisherDOI

• Automatic Synthesis of Visualization Design Knowledge Bases

  Open MIND · 2026-01-27

  preprintSenior author

  Formal representations of the visualization design space, such as knowledge bases and graphs, consolidate design practices into a shared resource and enable automated reasoning and interpretable design recommendations. However, prior approaches typically depend on fixed, manually authored rules, making it difficult to build novel representations or extend them for different visualization domains. Instead, we propose data-driven methods that automatically synthesize visualization design knowledge bases. Specifically, our methods (1) extract candidate design features from a visualization corpus, (2) select features forward and backward, and (3) render the final knowledge base. In our benchmark evaluation compared to Draco 2, our synthesized knowledge base offers general and interpretable design features and improves the accuracy of predicting effective designs by 1-15% in varied training and test sets. When we apply our approach to genomics visualization, the synthesized knowledge base includes sensible features with accuracy up to 97%, demonstrating the applicability of our approach to other visualization domains.

  DOI

• Just-In-Time Objectives: A General Approach for Specialized AI Interactions

  2026-04-13 · 1 citations

  articleOpen access

  Large language models promise a broad set of functions, but when not given a specific objective, they default to generic results. We demonstrate that inferring the user’s in-the-moment objective, then rapidly optimizing for that singular objective, enables LLMs to produce specialized tools, interfaces, and responses. Our work introduces just-in-time objectives, which model a user’s goals to specialize LLM systems on the fly. We contribute an architecture for automatically inducing such objectives by passively observing user behavior, then steering downstream AI systems through generation and evaluation against this objective. Inducing just-in-time objectives (e.g., “Clarify the abstract’s research contribution”) enables automatic generation of tools, e.g., those that critique a draft based on relevant HCI methodologies, anticipate related researchers’ reactions, or surface ambiguous terminology. In a series of experiments on participants’ own tasks, JIT objectives enable LLM outputs that achieve 66–86% win rates over typical LLMs. In-person use sessions confirm that JIT objectives produce specialized tools that are unique to each participant and are rated as significantly higher quality than a standard LLM chat tool.

  PublisherDOI

• Automatic Synthesis of Visualization Design Knowledge Bases

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Formal representations of the visualization design space, such as knowledge bases and graphs, consolidate design practices into a shared resource and enable automated reasoning and interpretable design recommendations. However, prior approaches typically depend on fixed, manually authored rules, making it difficult to build novel representations or extend them for different visualization domains. Instead, we propose data-driven methods that automatically synthesize visualization design knowledge bases. Specifically, our methods (1) extract candidate design features from a visualization corpus, (2) select features forward and backward, and (3) render the final knowledge base. In our benchmark evaluation compared to Draco 2, our synthesized knowledge base offers general and interpretable design features and improves the accuracy of predicting effective designs by 1–15% in varied training and test sets. When we apply our approach to genomics visualization, the synthesized knowledge base includes sensible features with accuracy up to 97%, demonstrating the applicability of our approach to other visualization domains.

  PublisherDOI

• GeoVisA11y: An AI-based Geovisualization Question-Answering System for Screen-Reader Users

  Open MIND · 2026-03-08

  preprint

  Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual and contextual queries. Through user studies with 12 screen-reader users and sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.

  DOI

• GeoVisA11y: An AI-based Geovisualization Question-Answering System for Screen-Reader Users

  ArXiv.org · 2026-03-08

  articleOpen access

  Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual and contextual queries. Through user studies with 12 screen-reader users and sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.

  PublisherOA PDF

• Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models

  2026-03-03 · 2 citations

  articleOpen access

  The output quality of large language models (LLMs) can be improved via “reasoning”: generating segments of chain-of-thought (CoT) content to further condition the model prior to producing user-facing output. While these chains contain valuable information, they are verbose and lack explicit organization, making them tedious to review. Moreover, they lack opportunities for user feedback, such as removing unwanted considerations, adding desired ones, or clarifying unclear assumptions. We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics and enables user review and modification. We implement interactive reasoning in Hippo, a prototype for AI-assisted decision making in the face of uncertain trade-offs. In a user study with 16 participants, we find that interactive reasoning in Hippo allows users to quickly identify and interrupt erroneous generations, efficiently steer the model towards customized responses, and better understand both model reasoning and model outputs. Our work contributes to a new paradigm that incorporates user oversight into LLM reasoning processes.

  PublisherDOI

• Crossing the Chasm: Bridging Visual Augmentations and Designer Intent

  IEEE Transactions on Visualization and Computer Graphics · 2026-01-01

  articleSenior author

  To direct attention and communicate context, visualization designers often employ augmentations such as annotations, animated transitions, and stylistic layouts. While graphical perception principles can helpfully prescribe effective low-level representations of data, they lack guidance for augmentations that service higher-level communication goals. To bridge this gap, we contribute a design space that frames designer intent as viewer-oriented cognitive behaviors, grounding communicative aims in actionable visualization design techniques, including both visual encodings and augmentations. This design space consists of common augmentation tactics (annotation, animation, stylized encodings, etc.) that implement design strategies (spotlighting, sequencing, association, etc.) to achieve higher-level design goals (observe, interpret, introspect), often simultaneously. We demonstrate the analytic and generative value of our design space with examples across varied designer objectives. We discuss how our contributions help align designer intent with reader takeaways, pave the way for readers to learn more effectively, and enable future (semi-)automated systems to support visualization designers in achieving their communication goals.

  PublisherDOI

• Mosaic: An Architecture for Linking Databases and Scalable Interactive Visualizations

  2025-06-17

  articleOpen access1st authorCorresponding
  PublisherDOI

Recent grants

• III: Medium: Collaborative Research: Composing Interactive Data Visualizations

  NSF · $240k · 2016–2020

• HCC: Small: Graphical Perception Revisited: Developing and Validating Design Guidelines for Data Visualization

  NSF · $250k · 2010–2013

• III: Large: Collaborative Research: Analysis Engineering for Robust End-to-End Data Science

  NSF · $1.6M · 2019–2026

• CHS: Small: Collaborative Research: Representing and Learning Visualization Design Knowledge

  NSF · $250k · 2019–2023

• DC: Medium: Collaborative Research: Data Intensive Computing: Scalable, Social Data Analysis

  NSF · $333k · 2010–2013

Frequent coauthors

• Dominik Moritz

  Carnegie Mellon University

  25 shared

• Arvind Satyanarayan

  Vassar College

  19 shared

• Maneesh Agrawala

  16 shared

• Christopher D. Manning

  15 shared

• Kanit Wongsuphasawat

  15 shared

• Joseph M. Hellerstein

  University of California, Berkeley

  15 shared

• Tim Althoff

  12 shared

• Diana MacLean

  11 shared

Labs

• Jeffrey Heer LabPI

Education

• B.S.

  UC Berkeley

• M.S.

  UC Berkeley

• Ph.D.

  UC Berkeley

Awards & honors

• MIT Technology Review's TR35 (2009)
• Sloan Fellowship (2012)
• ACM Grace Murray Hopper Award (2016)
• IEEE Visualization Technical Achievement Award (2017)
• induction into the IEEE Visualization (2019)