Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.


Visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, biomedical data analysis, healthcare data analysis, weather data analysis, poetry, network and graph analysis, financial data analysis, etc.

Research involves novel algorithm and technique development to building tools and systems that assist in the comprehension of massive amounts of (scientific) data. We also research the process of creating successful visualizations.

We strongly believe in the role of interactivity in visual data analysis. Therefore, much of our research is concerned with creating visualizations that are intuitive to interact with and also render at interactive rates.

Visualization at SCI includes the academic subfields of Scientific Visualization, Information Visualization and Visual Analytics.


Charles Hansen

Volume Rendering
Ray Tracing

Valerio Pascucci

Topological Methods
Data Streaming
Big Data

Chris Johnson

Scalar, Vector, and
Tensor Field Visualization,
Uncertainty Visualization

Mike Kirby

Uncertainty Visualization

Ross Whitaker

Topological Methods
Uncertainty Visualization

Miriah Meyer

Information Visualization
alex lex

Alex Lex

Information Visualization

Bei Wang

Information Visualization
Scientific Visualization
Topological Data Analysis

Centers and Labs:

Funded Research Projects:

Publications in Visualization:

Spatio-Temporal Visualization of Interdependent Battery Bus Transit and Power Distribution Systems
A. Bagherinezhad, M. Young, Bei Wang, M. Parvania. In IEEE PES Innovative Smart Grid Technologies Conference(ISGT), IEEE, 2021.

The high penetration of transportation electrification and its associated charging requirements magnify the interdependency of the transportation and power distribution systems. The emergent interdependency requires that system operators fully understand the status of both systems. To this end,a visualization tool is presented to illustrate the inter dependency of battery bus transit and power distribution systems and the associated components. The tool aims at monitoring components from both systems, such as the locations of electric buses, the state of charge of batteries, the price of electricity, voltage, current,and active/reactive power flow. The results showcase the success of the visualization tool in monitoring the bus transit and power distribution components to determine a reliable cost-effective scheme for spatio-temporal charging of electric buses.

TopoAct: Visually Exploring the Shape of Activations in Deep Learning
A. Rathore, N. Chalapathi, S. Palande, Bei Wang. In Computer Graphics Forum, Vol. 40, No. 1, pp. 382-397. 2021.

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e., combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct, a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.

Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data.
Y. Zhou, N. Chalapathi, A. Rathore, Y. Zhao, Bei Wang. In IEEE Pacific Visualization Symposium, 2021.

The mapper algorithm is a popular tool from topological data analysis for extracting topological summaries of high-dimensional datasets. In this paper, we present Mapper Interactive, a web-based framework for the interactive analysis and visualization of high-dimensional point cloud data. It implements the mapper algorithm in an interactive, scalable, and easily extendable way, thus supporting practical data analysis. In particular, its command-line API can compute mapper graphs for 1 million points of 256 dimensions in about 3 minutes (4 times faster than the vanilla implementation). Its visual interface allows on-the-fly computation and manipulation of the mapper graph based on user-specified parameters and supports the addition of new analysis modules with a few lines of code. Mapper Interactive makes the mapper algorithm accessible to nonspecialists and accelerates topological analytics workflows.

Loon: Using Exemplars to Visualize Large Scale Microscopy Data
D. Lange, E. Polanco, R. Judson-Torres, T. Zangle, A. Lex. In OSF Preprints, 2021.

Which drug is most promising for a cancer patient? This is a question a new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, to adjust filters, and remove noise, and for the analysis of the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both, derived data such as growth rates, and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach of choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.

Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts
W. Usher, X. Huang, S. Petruzza, S. Kumar, S. R. Slattery, S. T. Reeve, F. Wang, C. R. Johnson,, V. Pascucci. In IPDPS, 2021.

Investigating the Use of In Situ Reduction via Lagrangian Representations for Cosmology and Seismology Applications
S. Sane, C.R. Johnson, H. Childs. In ICCS 2021, 2021.

Evaluation of GPU Volume Rendering in PyTorch Using Data-Parallel Primitives
N. Marshak, P. Grosset, A. Knoll, J. P. Ahrens,, C. R. Johnson. In Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), 2021.

Visualization of Uncertain Multivariate Data via Feature Confidence Level-Sets
S. Sane, T. Athawale,, C.R. Johnson. In EuroVis 2021, 2021.

HyperLabels---Browsing of Dense and Hierarchical Molecular 3D Models
D Kouřil, T Isenberg, B Kozlíková, M Meyer, E Gröller, I Viola. In IEEE transactions on visualization and computer graphics, IEEE, 2021.
DOI: 10.1109/TVCG.2020.2975583

We present a method for the browsing of hierarchical 3D models in which we combine the typical navigation of hierarchical structures in a 2D environment---using clicks on nodes, links, or icons---with a 3D spatial data visualization. Our approach is motivated by large molecular models, for which the traditional single-scale navigational metaphors are not suitable. Multi-scale phenomena, e. g., in astronomy or geography, are complex to navigate due to their large data spaces and multi-level organization. Models from structural biology are in addition also densely crowded in space and scale. Cutaways are needed to show individual model subparts. The camera has to support exploration on the level of a whole virus, as well as on the level of a small molecule. We address these challenges by employing HyperLabels: active labels that---in addition to their annotational role---also support user interaction. Clicks on HyperLabels select the next structure to be explored. Then, we adjust the visualization to showcase the inner composition of the selected subpart and enable further exploration. Finally, we use a breadcrumbs panel for orientation and as a mechanism to traverse upwards in the model hierarchy. We demonstrate our concept of hierarchical 3D model browsing using two exemplary models from meso-scale biology.

reVISit: Looking Under the Hood of Interactive Visualization Studies
C. Nobre, D. Wootton, Z. T. Cutler, L. Harrison, H. Pfister, A. Lex. In SIGCHI Conference on Human Factors in Computing Systems (CHI), ACM, pp. 1--12. 2021.
DOI: 10.31219/

Quantifying user performance with metrics such as time and accuracy does not show the whole picture when researchers evaluate complex, interactive visualization tools. In such systems, performance is often influenced by different analysis strategies that statistical analysis methods cannot account for. To remedy this lack of nuance, we propose a novel analysis methodology for evaluating complex interactive visualizations at scale. We implement our analysis methods in reVISit, which enables analysts to explore participant interaction performance metrics and responses in the context of users' analysis strategies. Replays of participant sessions can aid in identifying usability problems during pilot studies and make individual analysis processes salient. To demonstrate the applicability of reVISit to visualization studies, we analyze participant data from two published crowdsourced studies. Our findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions.

Understanding a program's resiliency through error propagation
Z. Li, H. Menon, K. Mohror, P. T. Bremer, Y. Livant, V. Pascucci. In Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, ACM, pp. 362-373. 2021.

Aggressive technology scaling trends have worsened the transient fault problem in high-performance computing (HPC) systems. Some faults are benign, but others can lead to silent data corruption (SDC), which represents a serious problem; a fault introducing an error that is not readily detected nto an HPC simulation. Due to the insidious nature of SDCs, researchers have worked to understand their impact on applications. Previous studies have relied on expensive fault injection campaigns with uniform sampling to provide overall SDC rates, but this solution does not provide any feedback on the code regions without samples.

Blueprint: Cyberinfrastructure Center of Excellence
Subtitled “arXiv,” E. Deelman, A. Mandal, A. P. Murillo, J. Nabrzyski, V. Pascucci, R. Ricci, I. Baldin, S. Sons, L. Christopherson, C. Vardeman, R. F. da Silva, J. Wyngaard, S. Petruzza, M. Rynge, K. Vahi, W. R. Whitcup, J. Drake, E. Scott. 2021.

In 2018, NSF funded an effort to pilot a Cyberinfrastructure Center of Excellence (CI CoE or Center) that would serve the cyberinfrastructure (CI) needs of the NSF Major Facilities (MFs) and large projects with advanced CI architectures. The goal of the CI CoE Pilot project (Pilot) effort was to develop a model and a blueprint for such a CoE by engaging with the MFs, understanding their CI needs, understanding the contributions the MFs are making to the CI community, and exploring opportunities for building a broader CI community. This document summarizes the results of community engagements conducted during the first two years of the project and describes the identified CI needs of the MFs. To better understand MFs' CI, the Pilot has developed and validated a model of the MF data lifecycle that follows the data generation and management within a facility and gained an understanding of how this model captures the fundamental stages that the facilities' data passes through from the scientific instruments to the principal investigators and their teams, to the broader collaborations and the public. The Pilot also aimed to understand what CI workforce development challenges the MFs face while designing, constructing, and operating their CI and what solutions they are exploring and adopting within their projects. Based on the needs of the MFs in the data lifecycle and workforce development areas, this document outlines a blueprint for a CI CoE that will learn about and share the CI solutions designed, developed, and/or adopted by the MFs, provide expertise to the largest NSF projects with advanced and complex CI architectures, and foster a …

Lessons learned towards the immediate delivery of massive aerial imagery to farmers and crop consultants
A. A. Gooch, S. Petruzza, A. Gyulassy, G. Scorzelli, V. Pascucci, L. Rantham, W. Adcock, C. Coopmans. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI, Vol. 11747, International Society for Optics and Photonics, pp. 22 -- 34. 2021.
DOI: 10.1117/12.2587694

In this paper, we document lessons learned from using ViSOAR Ag Explorer™ in the fields of Arkansas and Utah in the 2018-2020 growing seasons. Our insights come from creating software with fast reading and writing of 2D aerial image mosaics for platform-agnostic collaborative analytics and visualization. We currently enable stitching in the field on a laptop without the need for an internet connection. The full resolution result is then available for instant streaming visualization and analytics via Python scripting. While our software, ViSOAR Ag Explorer™ removes the time and labor software bottleneck in processing large aerial surveys, enabling a cost-effective process to deliver actionable information to farmers, we learned valuable lessons with regard to the acquisition, storage, viewing, analysis, and planning stages of aerial data surveys. Additionally, with the ultimate goal of stitching thousands of images in minutes on board a UAV at the time of data capture, we performed preliminary tests for on-board, real-time stitching and analysis on USU AggieAir sUAS using lightweight computational resources. This system is able to create a 2D map while flying and allow interactive exploration of the full resolution data as soon as the platform has landed or has access to a network. This capability further speeds up the assessment process on the field and opens opportunities for new real-time photogrammetry applications. Flying and imaging over 1500-2000 acres per week provides up-to-date maps that give crop consultants a much broader scope of the field in general as well as providing a better view into planting and field preparation than could be observed from field level. Ultimately, our software and hardware could provide a much better understanding of weed presence and intensity or lack thereof.

Data-Driven Space-Filling Curves
L. Zhou, C. R. Johnson, D. Weiskopf. In IEEE Transactions on Visualization and Computer Graphics, Vol. 27, No. 2, IEEE, pp. 1591-1600. 2021.
DOI: 10.1109/TVCG.2020.3030473

We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization, including multivariate or comparative visualization,ensemble visualization of 2D and 3D data on regular grids, or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.

A Terminology for In Situ Visualization and Analysis Systems
H. Childs, S. D. Ahern, J. Ahrens, A. C. Bauer, J. Bennett, E. W. Bethel, P. Bremer, E. Brugger, J. Cottam, M. Dorier, S. Dutta, J. M. Favre, T. Fogal, S. Frey, C. Garth, B. Geveci, W. F. Godoy, C. D. Hansen, C. Harrison, B. Hentschel, J. Insley, C. R. Johnson, S. Klasky, A. Knoll, J. Kress, M. Larsen, J. Lofstead, K. Ma, P. Malakar, J. Meredith, K. Moreland, P. Navratil, P. O’Leary, M. Parashar, V. Pascucci, J. Patchett, T. Peterka, S. Petruzza, N. Podhorszki, D. Pugmire, M. Rasquin, S. Rizzi, D. H. Rogers, S. Sane, F. Sauer, R. Sisneros, H. Shen, W. Usher, R. Vickery, V. Vishwanath, I. Wald, R. Wang, G. H. Weber, B. Whitlock, M. Wolf, H. Yu, S. B. Ziegeler. In International Journal of High Performance Computing Applications, Vol. 34, No. 6, pp. 676–691. 2020.
DOI: 10.1177/1094342020935991

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over fifty experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.

Distributed Resources for the Earth System Grid Advanced Management (DREAM), Final Report
L. Cinquini, S. Petruzza, Jason J. Boutte, S. Ames, G. Abdulla, V. Balaji, R. Ferraro, A. Radhakrishnan, L. Carriere, T. Maxwell, G. Scorzelli, V. Pascucci. 2020.

The DREAM project was funded more than 3 years ago to design and implement a next-generation ESGF (Earth System Grid Federation [1]) architecture which would be suitable for managing and accessing data and services resources on a distributed and scalable environment. In particular, the project intended to focus on the computing and visualization capabilities of the stack, which at the time were rather primitive. At the beginning, the team had the general notion that a better ESGF architecture could be built by modularizing each component, and redefining its interaction with other components by defining and exposing a well defined API. Although this was still the high level principle that guided the work, the DREAM project was able to accomplish its goals by leveraging new practices in IT that started just about 3 or 4 years ago: the advent of containerization technologies (specifically, Docker), the development of frameworks to manage containers at scale (Docker Swarm and Kubernetes), and their application to the commercial Cloud. Thanks to these new technologies, DREAM was able to improve the ESGF architecture (including its computing and visualization services) to a level of deployability and scalability beyond the original expectations.

CPU Ray Tracing of Tree-Based Adaptive Mesh Refinement Data
F. Wang, N. Marshak, W. Usher, C. Burstedde, A. Knoll, T. Heister, C. R. Johnson. In Eurographics Conference on Visualization (EuroVis) 2020, Vol. 39, No. 3, 2020.

Adaptive mesh refinement (AMR) techniques allow for representing a simulation’s computation domain in an adaptive fashion. Although these techniques have found widespread adoption in high-performance computing simulations, visualizing their data output interactively and without cracks or artifacts remains challenging. In this paper, we present an efficient solution for direct volume rendering and hybrid implicit isosurface ray tracing of tree-based AMR (TB-AMR) data. We propose a novel reconstruction strategy, Generalized Trilinear Interpolation (GTI), to interpolate across AMR level boundaries without cracks or discontinuities in the surface normal. We employ a general sparse octree structure supporting a wide range of AMR data, and use it to accelerate volume rendering, hybrid implicit isosurface rendering and value queries. We demonstrate that our approach achieves artifact-free isosurface and volume rendering and provides higher quality output images compared to existing methods at interactive rendering rates.

Remembering Bill Lorensen: The Man, the Myth, and Marching Cubes
C. R. Johnson, T. Kapur, W. Schroeder,, T. Yoo. In IEEE Computer Graphics and Applications, Vol. 40, No. 2, pp. 112-118. March, 2020.
DOI: 10.1109/MCG.2020.2971168

Photographic High-Dynamic-Range Scalar Visualization
L. Zhou, M. Rivinius, C. R. Johnson,, D. Weiskopf. In IEEE Transactions on Visualization and Computer Graphics, Vol. 26, No. 6, IEEE, pp. 2156-2167. 2020.

We propose a photographic method to show scalar values of high dynamic range (HDR) by color mapping for 2D visualization. We combine (1) tone-mapping operators that transform the data to the display range of the monitor while preserving perceptually important features based on a systematic evaluation and (2) simulated glares that highlight high-value regions. Simulated glares are effective for highlighting small areas (of a few pixels) that may not be visible with conventional visualizations; through a controlled perception study, we confirm that glare is preattentive. The usefulness of our overall photographic HDR visualization is validated through the feedback of expert users.

In situ visualization of performance metrics in multiple domains
A. Sanderson, A. Humphrey, J. Schmidt, R. Sisneros,, M. Papka. In 2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), IEEE, Nov, 2019.
DOI: 10.1109/protools49597.2019.00014

As application scientists develop and deploy simulation codes on to leadership-class computing resources, there is a need to instrument these codes to better understand performance to efficiently utilize these resources. This instrumentation may come from independent third-party tools that generate and store performance metrics or from custom instrumentation tools built directly into the application. The metrics collected are then available for visual analysis, typically in the domain in which there were collected. In this paper, we introduce an approach to visualize and analyze the performance metrics in situ in the context of the machine, application, and communication domains (MAC model) using a single visualization tool. This visualization model provides a holistic view of the application performance in the context of the resources where it is executing.