Timothy Urness - research and publications

Just in Time Research: The Advantages and Pitfalls of a Student-led Interdisciplinary Undergraduate Research Experience
Journal of Computing Sciences in Colleges
Volume 33 Issue 5, May 2018
Matthew Zwier and Timothy Urness

This paper describes the advantages and potential pitfalls for interdisciplinary undergraduate research in computer science and chemistry programs. We contrast the traditional, mentor-led research approach in which a professor initiates and guides the research process vs. a student-led approach that requires the students define the research questions and goals in the process of conducting the research. We discuss our experiences with both approaches with an interdisciplinary team of students from the computer science department and chemistry department at Drake University, a private, small liberal arts college located in Des Moines, Iowa.

Predicting Political Parties Through Twitter and Machine Learning
Consortium for Computing Sciences in Colleges
Student Papers
Central Plains Conference
Maryville, Missouri, 2018
Matthew Antle, Chase Dooley, Julia Lanzel, and Timothy Urness

With the rise in the use of social media outlets by many politicians throughout the United States, many researchers and pollsters are turning to Twitter to track trends among both politicians and US citizens to predict the future of politics in this country. This research involves looking at Congress men and women’s filtered tweets that took place at any point within the last year and determining, through machine learning and use of neural networks, if their political party could be predicted on that basis only.

A Hybrid Open/Closed Lab for CS 1
ITiCSE: International Conference on Innovation and Technology in Computer Science
Proceedings of ITiCSE 2017
Timothy Urness

In this paper we introduce hybrid labs, an alternative to open or closed labs for CS 1, in which a set of written instructions, demonstration of techniques, and code examples are provided to students in lieu of a lecture. Students are presented with the lab two days prior to a class period and are given an option of submitting solutions to the challenges on their own time (similar to an open lab) or attending the class in which an instructor is available to provide additional help as needed (similar to a closed lab). Surveys show that students found the hybrid labs to be more engaging and preferred the hybrid labs over lectures as means of instruction. Furthermore, instructors found that the hybrid labs allowed for more tailored, individualized instruction for a variety of student abilities.
presentation slides

Graffinity: Visualizing Connectivity in Large Graphs
Eurovis 2017
Computer Graphics Forum (EuroVis '17), 2017.
Ethan Kerzner, Alexander Lex, Crystal Lynn Sigulinsky, Timothy Urness, Bryan William Jones, Robert E. Marc, Miriah Meyer

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Graffinity provides two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow.
instructional video

Predicting 2016 U.S. Presidential Candidate Success using Twitter and Maching Learning
Consortium for Computing Sciences in Colleges
Student Papers
Central Plains Conference
Lincoln, Nebraska, 2017
Jennifer Steffens, Alexis Kulash, Eric Manley, and Timothy Urness

Many researchers and pollsters are seeking to harness the vast amount of data available through various social media networks to make predictions and forecast trends in the population. Due to Twitter’s rising prominence in the political arena — both professionally and personally — this research sought to determine if one could accurately predict U.S. presidential candidate success on a primary-by-primary basis by analyzing regional tweet data. This involves collecting geotagged tweets filtered for political discussion during the primaries in both Iowa and New Hampshire and then running various machine learning algorithms upon those tweets in order to make a prediction as to which candidates received more than 10% of the vote for each county in each particular state.

Using Interview Questions as Short-Term Programming Assignments in CS2
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 32 Issue 5, 2017
Timothy Urness

Several books have been published that chronicle popular questions that have been asked on technical interviews at companies such as Microsoft, Google, Apple, Facebook, and Amazon. The books are written as a service to professionals interviewing for a job, but they also serve as an excellent set of short exemplar questions from concepts that are typically covered in an introductory programming sequence. We found that the programming assignments based on interview questions were particularly motivating for students. The interview questions, given in short-term programming assignments throughout the semester, resulted in an increased performance on midterm exams and final exams when compared to the performance of students in a section that utilized standard programming assignments.

SIGCSE Nifty Assignment: Movie Review Sentiment Analysis
SIGCSE: ACM Special Interest Group on Computer Science Education
Proceedings of SIGCSE 2016
Eric Manley and Timothy Urness

This assignment uses movie reviews from the Rotten Tomatoes database to do some simple sentiment analysis. Students will write programs that use the review text and a manually labeled review score to automatically learn how negative or positive the connotations of a particular word are. This can then be used to predict the sentiment of new text with reasonably good results. For example, student programs will be able to read text like this:
    The film was a breath of fresh air.
and predict that it is a positive review while predicting negative sentiment for text like this:
    It made me want to poke out my eyeballs.
The data (with some pre-processing from us) is from a Sentiment Analysis project at Stanford (which used a much more sophisticated algorithm) and has been used for a Kaggle machine learning competition.
We have provided two examples of projects based on this idea that we have used in a CS 1 course and a CS 2 course, though there are many extensions that could be made for these or other higher-level courses.

Incorporating Data Visulization in a Course on Computer Graphics
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 31 Issue 5, 2016
Timothy Urness

Data visualization techniques and tools can facilitate the identification of meaningful correlations within data as well as exploring new models and theories. Visualization has become an integral component for computer science programs that support data analytics or "big data" curriculums. This paper describes an introductory computer graphics course that incorporates learning objectives outlined in the ACM-IEEE curriculum guidelines for a unit on data visualization. We identify challenges related to teaching an introduction to computer graphics course, particularly at small-to-medium sized universities, and propose a unit on data visualization within an introductory computer graphics course with example assignments and student work.

Do Students Know What They Think They Know? Assessing Student Confidence in a Computer Graphics Course
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 31 Issue 5, 2016
Timothy Urness

The primary goal of this research is to determine if there are components of a course in which students have built up an overconfidence - they think they know the material more than they actually do or can demonstrate; or, conversely, an under-confidence - they are intimidated by the concept, but can actually perform the tasks to a greater level than their expectations. A secondary goal is to assess the effectiveness of the course in terms of the ability of the students to perform tasks related to the learning objectives and their perceptions. We found that the aggregate student confidence levels in the post-course exam were consistent with the corresponding final exam scores. However, students tended to have a slight overconfidence in material that was covered in the beginning of the semester.

Identifying Splice Sites Of Messenger RNA Using Support Vector Machines
Midwest Instruction and Computing Symposium
Proceedings of MICS 2016
Paige Diamond, Zachary Elkins, Kayla Huff, Lauren Naylor, Sarah Schoeberle, Shannon White, Timothy Urness, and Matthew Zwier
This paper presents two computational algorithms that can predict splicing patterns in gene expressions within eukaryotic cells. One approach utilizes Inverse Document Frequency scoring to identify keywords in the base pair sequences. Each sequence is encoded as a vector and then fed into a support vector machine (SVM) that determines the best separation between the positive and negative sequences. Another approach uses a method based on the Naive Bayes algorithm. This SVM algorithm takes in strings of base pairs from a genome sequence and encodes them as a unique 4­digit identifier of 1s and 0s. The SVM linearly separates the positive and negative examples.

Utilizing Machine Learning to Accelerate Automated Assignment of Backbone NMR Data
American Journal of Undergraduate Research
American Journal of Undergraduate Research, Volume 13 Issue 1, 2016
Joel Venzke, David Mascharka, Paxten Johnson, Rachel Davis, Katie Roth, Leah Robison, Adina Kilpatrick, and Timothy Urness

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for determining three-dimensional structures of biomolecules, including proteins. The protein structure determination process requires measured NMR values to be assigned to specific amino acids in the primary protein sequence. Unfortunately, current manual techniques for the assignment of NMR data are time-consuming and susceptible to error. The algorithm described in this paper addresses the challenges of previous programs by utilizing machine learning to predict amino acid type, thereby increasing assignment speed. The program also generates place-holders to accommodate missing data and amino acids with unique chemical characteristics, namely proline. Through machine learning and residue-type tagging, the assignment process is greatly sped up, while maintaining high accuracy.

On Video-Based Instruction for Introductory Computer Programming
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 29 Issue 5, 2014
Eric Manley and Timothy Urness

Since the use of video is critical to some pedagogies, the question of how it impacts student attitudes and learning is important. This study investigates this by looking at experiences in the programming unit within two sections of a broad-scope CS0 course, one of which used video-based instruction while the other did not. We found that students in the video section had a more positive view of the learning activities and thought their student-instructor interactions were more meaningful. Student performance data also suggests that video instruction may benefit student learning as well.

Accelerating Biomolecular Nuclear Magnetic Resonance Assignment with A*
Midwest Instruction and Computing Symposium
Proceedings of MICS 2014
Joel Venzke, Paxten Johnson, Rachel Davis, John Emmons, Katherine Roth, David Mascharka, Leah Robison, Timothy Urness and Adina Kilpatrick
Accurate assignment of nontrivial NMR datasets will provide the necessary framework for continued advancement in the fields of structural biology and proteomics. The algorithm presented in this paper utilizes A* search to sequence amino acids produced from NMR datasets of proteins.

Generating Interest in Computer Science Through Middle-School Android Summer Camps
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 28 Issue 5, 2013
Timothy Urness and Eric Manley

We conducted a week-long summer camp to promote interest in computer science among middle-school students. The camp primarily used self-paced video tutorials to teach programming concepts using the App Inventor for Android programming environment. Based on strong interest from students and parents as well as positive survey feedback, we conclude that the camp was very successful.

Automated Assignment of Backbone NMR Data using Artificial Intelligence
Midwest Instruction and Computing Symposium
Proceedings of MICS 2013
John Emmons, Steven Johnson, Timothy Urness, and Adina Kilpatrick
Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for the investiga- tion of three-dimensional structures of biological molecules such as proteins. One of the major challenges of the post-genomic era is to obtain structural and functional information on the many unknown proteins encoded by thousands of newly identified genes. The goal of this research is to design an algorithm capable of automating the analysis of backbone protein NMR data by implementing AI strategies such as greedy and A* search.

Building a Thriving CS Program at a Small Liberal Arts College
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 26 Issue 5, 2011
Timothy Urness and Eric Manley

In this paper we describe several techniques that have helped increase enrollment in the computer science program from 23 computer science majors in 2008 to 42 computer science majors in 2010 — an increase of 82.6% We discuss issues related to curriculum, programming assignments, and professor-student interactions that have made the discipline more attractive and manageable to a variety of students within the setting of a small liberal arts college.

Multivariate Visualization of Chromatographic Systems
Visualization and Data Analysis 2011
Proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 7868-11
Timothy Urness, Thomas Marrinan, Andrew R. Johnson, and Mark F. Vitha

Chromatography is a technique used to separate and quantify the components in a complex chemical mixture. We have created a 3D visualization system capable of comparing the chemical properties of chromatographic systems. The visualization system combines scatter plots, parallel coordinates, and specialized glyphs to assist in the analysis of chromatographic data and comparisons of multiple systems. Using this tool, numerous separation systems can be readily compared simultaneously — greatly facilitating the ability to select systems that are likely to produce desired separations during method development.

Understanding and Interpreting Multivalued Astronomical Data
IEEE Computer Graphics and Applications, September/October 2010
Thomas Marrinan, Timothy Urness, Charles Nelson, Kory Kreimeyer, and Jordan Mirocha

This article discusses how data visualization can help astronomers understand the kinematics of ionized gas in the nuclear regions of Seyfert galaxies, the most common active galactic nuclei. The goal is to compare spectroscopic-analysis results with the expectations from various gas flow models to determine the dominant acceleration mechanism.

System Selectivity Cube: A 3D Visualization Tool for Comparing the Selectivity of Gas Chromatography, Supercritical-Fluid Chromatography, High-Pressure Liquid Chromatography, and Micellar Electrokinetic Capillary Chromatography Systems
Analytical Chemistry, Volume 82, Issue 14
Andrew R. Johnson, Mark F. Vitha, Timothy Urness and Thomas Marrinan

A three-dimensional visualization tool termed the system selectivity cube (SSC) has been developed to aid in the selection of chromatography systems. The most effective way to change the resolution of a complex mixture is to change the selectivity of the separation. The SSC allows efficient identification of systems of differing selectivity. The SSC groups those systems which provide little or no difference in selectivity and those systems which do differ in their selectivities. We anticipate that this approach will be useful for selecting replacement columns, systems that may offer better results for difficult separations, and orthogonal phases for 2D gas chromatography (GC) and reversed phase liquid chromatography (RPLC) separations.


How Bob Barker Would (Probably) Teach Discrete Mathematics
PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies, Volume 20, Issue 6
Timothy Urness

This paper proposes a discrete mathematics course in which games from The Price Is Right are used to engage students in a deeper, practical study of discrete mathematics. The games themselves are not the focus of the course; rather, the mathematical principles of the games give motivation for the concepts being taught. The game examples are designed to be an alternative to traditional textbook problems and used as active exercises throughout the course.


Ionization of Atomic Hydrogen in Strong Infrared Laser Fields
Physical Review A, Volume 81, Issue 4
Alexei N. Grum-Grzhimailo, Brant Abeln, Klaus Bartschat, Daniel Weflen, and Timothy Urness

We have used the matrix iteration method of Nurhuda and Faisal [Phys. Rev. A 60, 3125 (1999)] to treat ionization of atomic hydrogen by a strong laser pulse. After testing our predictions against a variety of previous calculations, we present ejected-electron spectra as well as angular distributions for few-cycle infrared laser pulses with peak intensities of up to 1015 W/cm2. It is shown that the convergence of the results with the number of partial waves is a serious issue, which can be managed in a satisfactory way by using the velocity form of the electric dipole operator in connection with an efficient time-propagation scheme.

GPU Programming for Mathematical and Scientific Computing
Midwest Instruction and Computing Symposium
Proceedings of MICS 2010
Ethan Kerzner and Timothy Urness
Received Award for Best Student Paper

Graphical processing units used for mathematical and scientific computing are known as general purpose graphical processing units (GPGPUs). This paper is an introduction to the most popular GPGPU technology, NVIDIA's Compute Unified Device Architecture (CUDA). We approach CUDA from the perspective of a software developer, discussing the structure and organization of programs to explain the function of the GPU. We propose a potential application of GPGPU programming, the parallel analysis of a Hidden Markov Model, and discuss arithmetic error on the graphical processing unit.

Discrete Logarithms and Elliptic Curves in Cryptography
Midwest Instruction and Computing Symposium
Proceedings of MICS 2010
Derek Olson and Timothy Urness

This paper surveys the mathematical foundations, shortcomings, and novel variants of the first public key cryptosystem envisioned by Whitfield Diffie, Martin Hellman, and Ralph Merkle in 1976. The system they developed, Diffie-Hellman key exchange, relied on the difficulty of taking discrete logarithms in finite fields. While relatively secure, methods known as the index calculus exist to crack Diffie-Hellman key exchange in less than exponential running time. This has led to the use of elliptic curves in analogous cryptosystems. The basic theory underlying these elliptic curve cryptosystems is presented as well as a comparison of these systems with standard RSA encryption.

Assessment using Peer Evaluations, Random Pair Assignment, and Collaborative Programming in CS1
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 25 Issue 1
Timothy Urness

A common concern of professors implementing collaborative (pair) programming is the potential for a student to not actively participate in the programming process. In this paper I describe a technique for student assessment that uses peer evaluation and random pair assignment in collaborative programming assignments in CS1. The results showed that the assignment quality greatly increased and exam scores were comparable compared to previous course offerings when assignments were completed individually.

Using Daily Student Presentations to Address Attitudes and Communication Skills in CS1
SIGCSE: ACM Special Interest Group on Computer Science Education
Proceedings of SIGCSE 2009
Chris Bennett and Timothy Urness

Many CS1 courses lack a breadth in coverage of computing related topics and fail to help students develop oral communication skills. In this paper, we describe our experience with addressing these issues in CS1 at two different institutions through the use of brief, daily student presentations. We also describe the results of a survey students take before and after the course to evaluate how participating in the course can affect attitudes and beliefs about computer science.

Using Recycled Computers to Construct a Beowulf Cluster for Molecular Modeling
Midwest Instruction and Computing Symposium
Proceedings of MICS 2009
Creston Flemming, Timothy Urness, and Maria Bohorquez

We describe the process of using recycled computers to construct a Beowulf cluster to simulate the molecular dynamics of a chemical system. The cluster was assembled using computers taken out of service due to upgrades and would have otherwise been discarded. When assembled in a cluster, the computers performed the numerically-intensive process of molecular modeling roughly four times faster than a current machine.

Exploring Cache Optimization for Bioinformatics Applications
Midwest Instruction and Computing Symposium
Proceedings of MICS 2009
Shannon Dybvig, Megan Bailey, and Timothy Urness

Bioinformatics is one of the fastest growing fields due to its long-term medical and genetic implications. This paper explores the benefits of splitting cache and creating an optimal split in cache for bioinformatics applications.

Visualization of Energy Minimization in Ferromagnetic Systems
Midwest Instruction and Computing Symposium
Proceedings of MICS 2008
Zachary Oler and Timothy Urness
Received Award for Best Student Paper

Many different studies of magnetism models have presented theories on energy minimization. These studies, however, do not give a visual confirmation of what is occurring during minimization. In this paper, we describe a model and visualization system designed to illustrate the principles of energy minimization in magnetic systems.

Teaching File Input/Output, Loops, and If-Statements via a Red Eye Reduction Assignment
Consortium for Computing Sciences in Colleges
Journal of Computing Sciences in Colleges, Volume 23 Issue 4
Timothy Urness

This paper describes a "nifty" programming assignment that requires students to use files, loops, and if-statements to implement an algorithm that will remove the red-eye artifact from an image. The assignment is most suitable for a CS1 course, but could be altered to accommodate a CS0 or CS2 course.

Streamline Visualization of Multiple 2D Vector Fields
Visualization and Data Analysis 2008
Proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 6809-9
Timothy Urness and Victoria Interrante

The analysis of data that consists of multiple vector fields can be greatly facilitated by the simultaneous visualization of the vector fields. An effective visualization must accurately reflect the key physical structures of the fields in a way that does not allow for an unintended bias towards one distribution. While there are several effective techniques to visualize a single vector field through equally-spaced streamlines, applying these techniques to individual vector fields and combining them in a single image yields several undesirable artifacts. In this paper, we present strategies for the effective visualization of two vector fields through the use of streamlines.

Teaching Computer Organization/Architecture by Building a Computer
2007 Workshop on Computer Architecture Education
Timothy Urness

This paper describes a series of exercises and assignments suggested for an introductory computer organization or computer architecture course. The primary objective of these exercises is to engage a class of students by introducing the practical, hands-on application of assembling a computer by selecting and purchasing individual components.

FieldVis: A Tool for Visualizing Astrophysical Magnetohydrodynamic Flow
IEEE Computer Graphics and Applications, January/February 2007
Blayne Field, Sean O'Neill, Timothy Urness, Victoria Interrante, and Thomas W. Jones

Our group is involved in magnetohydrodynamic simulations that track the time and space evolution of the full 3D velocity and magnetic vector fields, plus fundamental scalar fields such as density and pressure. To accomplish the complex visualization of these jets, we developed FieldVis, a simulation tool that focuses primarily on representing 3D vector and scalar fields.

Directional Enhancement in Texture-based Vector Field Visualization
Graphite 2006
Francesca Taponecco, Timothy Urness, and Victoria Interrante

The use of textures provides a rich and diverse set of possibilities for the visualization of flow data. We present methods designed to produce oriented and controlled textures that accurately reflect the complex patterns that occur in vector field visualizations.

Strategies for the Visualization of Multiple 2D Vector Fields
IEEE Computer Graphics and Applications, July/August 2006
Timothy Urness, Victoria Interrante, Ellen Longmire, Ivan Marusic, Sean O'Neill, and Thomas W. Jones

Strategies for effectively visualizing co-located 2D vector fields enable understanding of key physical structures of one vector field within the context of a related vector field. We describe the range of effects possible by combining several existing flow visualization techniques for analyzing multiple vector fields.

Texture-Based Visualization of Multi-Field Flow Data
Ph.D. Dissertation, May 2006
University of Minnesota
Department of Computer Science and Engineering
Timothy Urness

The goal through this work is to enable researchers to obtain a succinct, meaningful visual summary of the contents of a dataset that consists of multiple, coincident variables. This is accomplished through providing techniques that allow the creation of an image in which the important features of multiple scalar or vector fields can be understood both independently and in the context of the other fields.

Techniques for Visualizing Multi-Valued Flow Data
Eurographics/IEEE TCVG Symposium on Visualization 2004
Timothy Urness, Victoria Interrante, Ellen Longmire, Ivan Marusic, and Bharathram Ganapathisubramani

We discuss several techniques to display scalar distributions within an image depicting a 2D flow field. We address how contrast and luminance can effectively be used, present modifications to an algorithm that uses dense streamlines to represent flow direction, and present a new technique, based on embossing, to encode the out-of-plane component of a 3D vector field over a 2D domain.

Effectively Visualizing Multi-Valued Flow Data Using Color and Texture
IEEE Visualization 2003
Timothy Urness, Victoria Interrante, Ivan Marusic, Ellen Longmire, and Bharathram Ganapathisubramani

In this paper we offer several new insights and techniques for effectively using color and texture to simultaneously convey information about multiple 2D scalar and vector distributions, in a way that facilitates allowing each distribution to be understood both individually and in the context of one or more of the other distributions.

Effective Visualization of Stereo PIV Vector Fields of a Turbulent Boundary Layer
Journal of Turbulence, Article 23, Volume 4, 2003.
E. K. Longmire, B Ganapathisubramani, I Marusic, T Urness, and V Interrante

Stereo PIV datasets contain three dimensional information over a plane from which multiple quantities can be derived at each point. The task of visualizing these different parameters simultaneously is challenging and this inhibits our ability to analyze and derive firm conclusions about the physics of the flow. In this paper, we discuss several different ways in which the primary quantities can be viewed simultaneously in the same image.

Techniques for Visualizing Multi-Valued Flow Data
Master of Science Thesis, May 2003
University of Minnesota
Department of Computer Science and Engineering
Timothy Urness

We present several techniques to effectively visualize multi-valued flow data using contrast, color, 3D visualization, and texture. The ultimate goal through this work is to enable researchers to obtain a succinct, meaningful visual summary of the contents of a dataset through providing techniques that allow the creation of images in which the important features of multiple scalar distributions can be understood both independently and in the context of multiple other distributions.

Structure Identification and Analysis in Turbulent Boundary Layers by Stereo PIV
4th International Symposium on Particle Image Velocimetry, 2001
E. K. Longmire, B. Ganapathisubramani, I. Marusic, T. Urness

The objective of this study is to apply Stereo PIV in streamwise-spanwise planes to measure three-dimensional velocity fields and to develop methods for the identification of typical flow structures. These methods can then be applied to quantify the strength, size, and frequency of various structures and therefore to examine the existence, nature, and symmetry of hairpin-like vortices and vortex packets.

Analyzing Industrial Furnace Efficiency Using Comparative Visualization in a Virtual Reality Environment
Proceedings of the 1999 ASME International Mechanical Engineering Congress and Exposition, pages 191-199.
Lori Frietag and Tim Urness

We describe an interactive toolkit used to perform comparative analysis of two or more data sets arising from numerical simulations. An industrial application aimed at designing an efficient, low-NOx burner for industrial furnaces is used. Critical insights are obtained by interactively adjusted color maps, data culling, and data manipulation. New paradigms for scaling small values in the data comparison technique are described. The display device used for this application was the CAVE virtual reality theater, and we describe the user interface to the visualization toolkit and the benefits of immersive 3D visualization for comparative analysis.