About

David H. Laidlaw is a Professor of Computer Science at Brown University, where he leads the Visualization Research Lab. His research focuses on the application of visualization, computational modeling, computer graphics, and computer science to various scientific disciplines. He has particular interests in the visualization of multivalued multidimensional imaging data, comparisons between virtual and non-virtual environments for scientific tasks, and the integration of art and perception principles into visualization techniques. Laidlaw earned his PhD in Computer Science from Caltech, where he worked in the graphics group, and completed post-doctoral research at Caltech's Beckman Institute in the Fraser lab within the Biology division. His group’s work is organized around several themes and projects that explore scientific visualization, and he collaborates with others in the field, including projects supported by NIH involving diffusion MRI data from adult volunteers. Laidlaw’s academic contributions include research, teaching, and service, with detailed information available through his CV and various publication listings.

Research topics

• Artificial Intelligence
• Computer Science
• Information Retrieval
• Psychology
• Human–computer interaction
• Literature
• Biology
• Environmental ethics
• Philosophy
• Art
• Cognitive psychology

Selected publications

• ChArtist: Generating Pictorial Charts with Unified Spatial and Subject Control

  ArXiv.org · 2026-03-15

  articleOpen access

  A pictorial chart is an effective medium for visual storytelling, seamlessly integrating visual elements with data charts. However, creating such images is challenging because the flexibility of visual elements often conflicts with the rigidity of chart structures. This process thus requires a creative deformation that maintains both data faithfulness and visual aesthetics. Current methods that extract dense structural cues from natural images (e.g., edge or depth maps) are ill-suited as conditioning signals for pictorial chart generation. We present ChArtist, a domain-specific diffusion model for generating pictorial charts automatically, offering two distinct types of control: 1) spatial control that aligns well with the chart structure, and 2) subject-driven control that respects the visual characteristics of a reference image. To achieve this, we introduce a skeleton-based spatial control representation. This representation encodes only the data-encoding information of the chart, allowing for the easy incorporation of reference visuals without a rigid outline constraint. We implement our method based on the Diffusion Transformer (DiT) and leverage an adaptive position encoding mechanism to manage these two controls. We further introduce Spatially Gated Attention to modulate the interaction between spatial control and subject control. To support the fine-tuning of pre-trained models for this task, we created a large-scale dataset of 30,000 triplets (skeleton, reference image, pictorial chart). We also propose a unified data accuracy metric to evaluate the data faithfulness of the generated charts. We believe this work demonstrates that current generative models can achieve data-driven visual storytelling by moving beyond general-purpose conditions to task-specific representations. Project page: https://chartist-ai.github.io/.

  PublisherOA PDF

• ChArtist: Generating Pictorial Charts with Unified Spatial and Subject Control

  arXiv (Cornell University) · 2026-03-15

  preprintOpen access

  A pictorial chart is an effective medium for visual storytelling, seamlessly integrating visual elements with data charts. However, creating such images is challenging because the flexibility of visual elements often conflicts with the rigidity of chart structures. This process thus requires a creative deformation that maintains both data faithfulness and visual aesthetics. Current methods that extract dense structural cues from natural images (e.g., edge or depth maps) are ill-suited as conditioning signals for pictorial chart generation. We present ChArtist, a domain-specific diffusion model for generating pictorial charts automatically, offering two distinct types of control: 1) spatial control that aligns well with the chart structure, and 2) subject-driven control that respects the visual characteristics of a reference image. To achieve this, we introduce a skeleton-based spatial control representation. This representation encodes only the data-encoding information of the chart, allowing for the easy incorporation of reference visuals without a rigid outline constraint. We implement our method based on the Diffusion Transformer (DiT) and leverage an adaptive position encoding mechanism to manage these two controls. We further introduce Spatially Gated Attention to modulate the interaction between spatial control and subject control. To support the fine-tuning of pre-trained models for this task, we created a large-scale dataset of 30,000 triplets (skeleton, reference image, pictorial chart). We also propose a unified data accuracy metric to evaluate the data faithfulness of the generated charts. We believe this work demonstrates that current generative models can achieve data-driven visual storytelling by moving beyond general-purpose conditions to task-specific representations. Project page: https://chartist-ai.github.io/.

  PublisherDOI

• General microstructure factor analysis of diffusion MRI in gray-matter predicts cognitive scores

  NeuroImage · 2026-05-19

  articleOpen accessSenior author

  Diffusion MRI has revealed important insights into white matter microstructure, but its application to gray matter remains comparatively less explored. Here, we investigate whether global patterns of gray-matter microstructure can be captured through neurite orientation dispersion and density imaging (NODDI) and whether such patterns are predictive of cognitive performance. Our findings demonstrate that PCA-based global indicators of gray-matter microstructure provide complementary markers of structure-function relationships, extending beyond region-specific analyses. Our results suggest that general microstructure factors may serve as robust, interpretable biomarkers for studying cognition and cortical organization at the population level. Using diffusion MRI and behavioral data from the Human Connectome Project Young Adult study, we derived region-averaged NODDI parameters and applied principal component analysis (PCA) to construct general gray-matter microstructure factors. We found that the factor derived from isotropic volume fraction explained substantial inter-individual variability and was significantly correlated with specific cognitive scores collected from the NIH Toolbox. In particular, the isotropic volume fraction factor was linked to reading and vocabulary performance and to cognitive fluidity.

  PublisherDOI

• Visual Exploration of a Historical Vietnamese Corpus of Captioned Drawings: A Case Study

  IEEE Computer Graphics and Applications · 2026-01-01

  article

  This paper presents a case study focusing on the exploratory visual analysis of a unique historical dataset consisting of approximately 4000 visual sketches and associated captions from an encyclopedic book published in 1909-1910. The book, which offers insight into Vietnamese crafts and social practices, poses the challenge of extracting cultural meaning and narrative structure from thousands of drawings and multilingual captions. Our research aims to explore and evaluate the effectiveness of multiple visualization techniques in uncovering meaningful relationships within the dataset while working closely with professional historians. The main contributions of this study include refining historical research questions through task and data abstraction, combining and validating visualization techniques for historical data interpretation, and involving a focus group of historians for further evaluation. These contributions offer generalizable insights for the development of domain-specific visualization tools and support interdisciplinary engagement in historical data visualization and critical digital humanities research.

  PublisherDOI

• Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-21

  preprintOpen access

  Motivation: We propose DRIFT, a framework that integrates spatial context into the input representations for foundation models by leveraging diffusion on spatial graphs derived from spatial transcriptomics (ST) data. ST captures gene expression profiles while preserving spatial context, enabling downstream analysis tasks such as cell-type annotation, clustering, and cross-sample alignment. However, due to its emerging nature, there are very few foundation models that can utilize ST data to generate embeddings generalizable across multiple tasks. Meanwhile, well-documented foundational models trained on large-scale single-cell gene expression (scRNA-seq) data have demonstrated generalizable performance across scRNA-seq assays, tissues, and tasks; however, they do not leverage the spatial information in ST data. We use heat kernel diffusion to propagate embeddings across spatial neighborhoods, incorporating the local neighborhood context of the ST data while preserving the transcriptomic representations learned by state-of-the-art single-cell foundation models. Results: We systematically benchmark five foundational models (both scRNA-seq and ST-based) across key ST tasks such as annotation, alignment, and clustering, ensuring a comprehensive evaluation of our proposed framework. Our results show that DRIFT significantly improves the performance of existing foundational models on ST data over specialized state-of-the-art methods. Overall, DRIFT is an effective, accessible, and generalizable framework that bridges the gap toward universal models for modeling spatial transcriptomics.

  PublisherOA PDFDOI

• Improved Spatial Transcriptomics Clustering with Nested Graph Neural Networks

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-08 · 1 citations

  preprintOpen access

  Abstract We introduce a novel approach, STING, for spatial transcriptomic clustering analysis. Unlike existing state-of-the-art techniques that use graph-based neural networks (GNNs) trained on graphs generated from the spatial proximity of tissue locations (or spots), STING incorporates spot-specific related genes. This feature allows STING to better distinguish between clusters and identify meaningful gene-gene relations for knowledge discovery. It is a nested GNN framework that simultaneously models gene-gene and spatial relations. Using the gene expression, we generate a spot-specific gene-gene co-expression graph. We implement an inner GNN for these graphs to generate embeddings for each location. Next, we utilize these embeddings as features in a sample-wide graph generated using spatial information. We implement an outer GNN for this graph to reconstruct the original gene expression data. Finally, STING is trained end-to-end to generate embeddings that capture gene-gene and spatial information, which we input to a clustering algorithm to produce the spatial clusters. Experiments for 26 samples across 7 datasets and 5 spatial sequencing technologies show that STING outperforms the existing state-of-the-art techniques with a 1.58% to 4.07% improvement in the clustering evaluation metric, thus confirming that integrating gene-gene relation information with the clustering task leads to more informative embeddings and better clusters. Furthermore, experiments on a human breast cancer dataset show that STING identifies relevant genes and gene-gene relations, enabling biological hypothesis generation.

  PublisherOA PDFDOI

• Predicting post-surgery change in visual acuity after successful repair of macula-off retinal detachments: findings from a large prospective UK cohort study

  Research Square · 2024-09-02

  preprintOpen access
  PublisherOA PDFDOI

• Evaluating Text Reading Speed in VR Scenes and 3D Particle Visualizations

  IEEE Transactions on Visualization and Computer Graphics · 2024-03-04 · 10 citations

  articleSenior author

  This work reports how text size and other rendering conditions affect reading speeds in a virtual reality environment and a scientific data analysis application. Displaying text legibly yet space-efficiently is a challenging problem in immersive displays. Effective text displays that enable users to read at their maximum speed must consider the variety of virtual reality (VR) display hardware and possible visual exploration tasks. We investigate how text size and display parameters affect reading speed and legibility in three state-of-the-art VR displays: two head-mounted displays and one CAVE. In our perception experiments, we establish limits where reading speed declines as the text size approaches the so-called critical print sizes (CPS) of individual displays, which can inform the design of uniform reading experiences across different VR systems. We observe an inverse correlation between display resolution and CPS. Yet, even in high-fidelity VR systems, the measured CPS was larger than in comparable physical text displays, highlighting the value of increased VR display resolutions in certain visualization scenarios. Our findings indicate that CPS can be an effective metric for evaluating VR display usability. Additionally, we evaluate the effects of text panel placement, orientation, and occlusion-reducing rendering methods on reading speeds in generic volumetric particle visualizations. Our study provides insights into the trade-off between text representation and legibility in cluttered immersive environments with specific suggestions for visualization designers and highlight areas for further research.

  PublisherDOI

• How Can Deep Neural Networks Aid Visualization Perception Research?

  2023-01-21

  preprintOpen accessSenior author

  How deep neural networks can aid visualization perception research is a wide-open question. This paper provides insights from three perspectives—prediction, generalization, and interpretation—via training and analyzing deep convolutional neural networks on human correlation judgments in scatterplots across three studies. The first study assesses the accuracy of twenty-nine neural network architectures in predicting human judgments, finding that a subset of the architectures (e.g., VGG-19) has comparable accuracy to the best-performing regression analyses in prior research. The second study shows that the resulting models from the first study display better generalizability than prior models on two other judgment datasets for different scatterplot designs. The third study interprets visual features learned by a convolutional neural network model, providing insights about how the model makes predictions, and identifies potential features that could be investigated in human correlation perception studies. Together, this paper suggests that deep neural networks can serve as a tool for visualization perception researchers in devising potential empirical study designs and hypothesizing about perpetual judgments. The preprint, data, code, and training logs are available at https://doi.org/10.17605/osf.io/exa8m.

  PublisherOA PDFDOI

• How Can Deep Neural Networks Aid Visualization Perception Research? Three Studies on Correlation Judgments in Scatterplots

  2023-04-19 · 11 citations

  articleSenior author

  How deep neural networks can aid visualization perception research is a wide-open question. This paper provides insights from three perspectives—prediction, generalization, and interpretation—via training and analyzing deep convolutional neural networks on human correlation judgments in scatterplots across three studies. The first study assesses the accuracy of twenty-nine neural network architectures in predicting human judgments, finding that a subset of the architectures (e.g., VGG-19) has comparable accuracy to the best-performing regression analyses in prior research. The second study shows that the resulting models from the first study display better generalizability than prior models on two other judgment datasets for different scatterplot designs. The third study interprets visual features learned by a convolutional neural network model, providing insights about how the model makes predictions, and identifies potential features that could be investigated in human correlation perception studies. Together, this paper suggests that deep neural networks can serve as a tool for visualization perception researchers in devising potential empirical study designs and hypothesizing about perpetual judgments. The preprint, data, code, and training logs are available at https://doi.org/10.17605/osf.io/exa8m.

  PublisherDOI

Recent grants

• CGV: Small: Collaborative Research: Immersive Visualization and 3D Interaction for Volume Data Analysis

  NSF · $250k · 2013–2017

• ITR: (ASE)-(sim+dmc+int): Computational Simulation, Modeling, and Visualization for Understanding Unsteady Bioflows

  NSF · $662k · 2004–2009

• NIH Grant R01EB00415503

  NIH · $525k · 2009

• NIH Grant R01EB004155

  NIH · $973k · 2012

• NIH Grant R01EB00415501A1

  NIH · $481k · 2009

Frequent coauthors

• Ryan P. Cabeen

  SignPath Pharma (United States)

  111 shared

• Robert Paul

  Missouri Institute of Mental Health

  88 shared

• Joseph J. Crisco

  Brown University

  81 shared

• Laurie M. Baker

  62 shared

• Lauren E. Salminen

  Imaging Center

  62 shared

• Stephen Correia

  Providence VA Medical Center

  60 shared

• Douglas C. Moore

  60 shared

• Thomas E. Conturo

  Mallinckrodt (United States)

  53 shared