Visualization Viewpoints Pathways for Theoretical Advances in Visualization

Total Page:16

File Type:pdf, Size:1020Kb

Visualization Viewpoints Pathways for Theoretical Advances in Visualization Editor: Visualization Viewpoints Theresa-Marie Rhyne Pathways for Theoretical Advances in Visualization Min Chen Jessie Kennedy University of Oxford Edinburgh Napier University Georges Grinstein Melanie Tory University of Massachusetts Tableau Software Chris R. Johnson University of Utah ore than a decade ago, Chris Johnson Visualization theory is commonly viewed as a re- proposed the “Theory of Visualization” search focus for only a few individual researchers. as one of the top research problems in Its outcomes, perhaps in the form of theorems or Mvisualization.1 Since then, there have been several laws, are perceived as too distant from practice to be theory-focused events, including three workshops useful. Perhaps inspired by well-known theoretical and three panels at IEEE Visualization (VIS) Con- breakthroughs in the history of science, visualiza- ferences. Together, these events have produced a tion researchers may unconsciously have high ex- set of convincing arguments: pectations for the originality, rigor, and significance of the theoretical advancements to be made in a ■ As in all scientific and scholarly subjects, theo- research project or presented in a research paper. retical development in visualization is a neces- On the contrary, although textbooks tend to sary and integral part of the progression of the attribute major breakthroughs to single pioneers subject itself. at specific times and places, in most cases, such ■ Theoretical developments in visualization can breakthroughs generally take years or even decades draw on theoretical advances in many disci- and are usually the product of numerous incremen- plines, including, for example, mathematics, tal developments, including a substantial number computer science, engineering science, psychol- of erroneous solutions suggested by the pioneers ogy, neuroscience, and the social sciences. themselves as well as many lesser-known individu- ■ Visualization holds a distinctive position, con- als. Many complex discoveries did not initially ap- necting human-centric processes (such as hu- pear to have elegant proofs, and it has taken some man perception, cognition, interaction, and challenging and often questionable steps to obtain communication) with machine-centric pro- the well-formulated solutions we find in modern cesses (such as statistics, algorithms, and ma- textbooks. For example, most readers associate the chine learning). It therefore provides a unique theory of general relativity with Albert Einstein’s platform to conduct theoretical studies that may November 1915 discovery in Berlin. According impact on other disciplines. to Petro Ferreira,2 Einstein first speculated about ■ Compared with many mature disciplines (such the generalization in 1907. He then published two as mathematics, physics, biology, psychology, and papers with Marcel Grossmann (Zurich) in 1913 philosophy), theoretical research activities in vi- that sketched out the theory and worked with sualization are sparse. The subject can therefore David Hilbert (Göttingen) on the problem in benefit from a significantly increased effort to June 1915. Some of the most important discover- make new theoretical advances. ies related to the theory of general relativity are Published by the IEEE Computer Society 0272-1716/16/$33.00 © 2016 IEEE IEEE Computer Graphics and Applications 103 Visualization Viewpoints Providing mathematical validation and numerical prediction Enabling measurement, abstraction, and deduction of laws Suggesting Providing context casual relations and structure Quantitative Taxonomies Principles Conceptual Theoretic laws and and ontologies and guidelines models frameworks theoretic systems Subset becoming a model Model becoming a system Transforming to axioms, theorems, and conjectures Providing denitions, categorization, and suggested relations Figure 1. A theoretical foundation typically evolves through iterative developments. The development of each aspect both influences and benefits from that of others. A successful transformation between different aspects indicates a theoretical enhancement of understanding. Note that the third aspect, “Conceptual Models and Theoretic Frameworks,” is represented by two boxes in the figure. Mercury’s perihelion shift (Le Verrier, 1859), the In this article, we first review four major aspects 1919 eclipse expedition (Edington, Cottingham, of a theoretical foundation and then discuss the Crommelin, and Davidson), the evolving universe interactions and transformations between them. (Fredmann, 1922; Lemaître, 1927), the expanding Figure 1 provides an overview of the discourse in universe (Slipper, 1915; Lundmark, 1924; Hubble this article. and Humason, 1929), the big bang (Lemaître, 1931), and the black hole (Schwarzschild 1916, Taxonomies and Ontologies Chandrasekhar, 1935; Landau, 1938; Oppenheimer For millennia, humans have been classifying and his students, 1939). Some ill-fated solutions things in the world around them by describing also followed Einstein’s 1915 discovery, the most and naming concepts to facilitate communication notable of which were perhaps the static universe (see the “History of Taxonomy and Ontology” (Einstein and de Sitter) and the suspended uni- sidebar). The most significant and enduring effort verse (Eddington). is the classification of life on Earth, which com- During an Alan Turing Institute event in Lon- menced in Aristotle’s time, became mainstream don in April 2016 on the theoretical foundation of through the work of Linnaeus, and continues to visual analytics, discussions on the need to build this day with new species being identified and al- such a theoretical foundation varied greatly, with ternative classifications of existing species being opinions ranging from “Visualization should not proposed.3 Alternative classifications taxonomies( ) be physics-envy” to “It is irresponsible for academ- arise over time as a result of differing opinions ics not to try.” After two days of presentations, about the importance of differentiating character- discussions, and debates, the attendees gradually istics used in creating the concepts (taxa). These converged on a common understanding that a differing opinions are usually the result of new theoretical foundation consists of several aspects information becoming available, often through (see the “Major Aspects of a Theoretical Founda- technological advances, which can result in the tion” sidebar for more details) and that every visu- same organism being classified according to dif- alization researcher should be able to make direct ferent taxonomic opinions and subsequently hav- contributions to some aspect of the theoretical ing several alternative names, which may in turn foundation of visualization. lead to miscommunication. Newer classifications During the IEEE VIS Conference in Baltimore, are usually improvements on previous ones, but Maryland, in October 2016, a discussion panel took sometimes the existence of alternative classifica- this viewpoint further by outlining avenues for pur- tions reflects a disagreement as to how to interpret suing theoretical research in each aspect. This ar- the data on which the classification is based. ticle is a structured reflection by the panelists about Ontologies are representations of different rela- the discussions during that IEEE VIS 2016 panel. tionships among various concepts. Naturally, they 104 July/August 2017 Major Aspects of a Theoretical Foundation are built on the taxonomic classification of the concepts of both entities and relationships. Tax- ttendees at an Alan Turing Institute event in London in April 2016 onomies and ontologies are means of conceptu- Aon the theoretical foundation of visual analytics reached a con- alizing, understanding, organizing, and reasoning sensus that a theoretical foundation consists of the following aspects. about these entities and relationships. They are central to communicating about the world around Taxonomies and Ontologies us. They play an increasing role in understanding In scientific and scholarly disciplines, a collection of concepts are in the visualization field, allowing us to organize commonly organized into a taxonomy or ontology. In the former, and formalize our knowledge. concepts are known as taxa and are typically arranged hierarchically A brief review of the literature over the past three using a tree structure. In the latter, concepts, often in conjunction decades reveals at least 70 publications containing with their instances, attributes, and other entities, are organized some form of visualization taxonomy.3 Three ques- into a schematic network, where edges represent various relations tions are relevant when considering visualization and rules. taxonomies: What is being classified (domain)? Why is the taxonomy being developed (purpose)? Principles and Guidelines How is the taxonomy constructed (process)? Tax- A principle is a law or rule that must be followed and is usually ex- onomies have been proposed to classify many as- pressed in a qualitative description. A guideline describes a process pects of visualization, including systems, tools, or a set of actions that may lead to a desired outcome or, alterna- techniques, interaction approaches, data types, tively, actions to be avoided to prevent an undesired outcome. The user tasks, visual encodings, input methods, and former usually implies a confidence in the high degree of generali- evaluation strategies. These aspects can be classi- ty and certainty of the causality concerned, whereas the latter sug-
Recommended publications
  • Computer Graphics and Visualization
    European Research Consortium for Informatics and Mathematics Number 44 January 2001 www.ercim.org Special Theme: Computer Graphics and Visualization Next Issue: April 2001 Next Special Theme: Metacomputing and Grid Technologies CONTENTS KEYNOTE 36 Physical Deforming Agents for Virtual Neurosurgery by Michele Marini, Ovidio Salvetti, Sergio Di Bona 3 by Elly Plooij-van Gorsel and Ludovico Lutzemberger 37 Visualization of Complex Dynamical Systems JOINT ERCIM ACTIONS in Theoretical Physics 4 Philippe Baptiste Winner of the 2000 Cor Baayen Award by Anatoly Fomenko, Stanislav Klimenko and Igor Nikitin 38 Simulation and Visualization of Processes 5 Strategic Workshops – Shaping future EU-NSF collaborations in in Moving Granular Bed Gas Cleanup Filter Information Technologies by Pavel Slavík, František Hrdliãka and Ondfiej Kubelka THE EUROPEAN SCENE 39 Watching Chromosomes during Cell Division by Robert van Liere 5 INRIA is growing at an Unprecedented Pace and is starting a Recruiting Drive on a European Scale 41 The blue-c Project by Markus Gross and Oliver Staadt SPECIAL THEME 42 Augmenting the Common Working Environment by Virtual Objects by Wolfgang Broll 6 Graphics and Visualization: Breaking new Frontiers by Carol O’Sullivan and Roberto Scopigno 43 Levels of Detail in Physically-based Real-time Animation by John Dingliana and Carol O’Sullivan 8 3D Scanning for Computer Graphics by Holly Rushmeier 44 Static Solution for Real Time Deformable Objects With Fluid Inside by Ivan F. Costa and Remis Balaniuk 9 Subdivision Surfaces in Geometric
    [Show full text]
  • Math 253: Mathematical Methods for Data Visualization – Course Introduction and Overview (Spring 2020)
    Math 253: Mathematical Methods for Data Visualization – Course introduction and overview (Spring 2020) Dr. Guangliang Chen Department of Math & Statistics San José State University Math 253 course introduction and overview What is this course about? Context: Modern data sets often have hundreds, thousands, or even millions of features (or attributes). ←− large dimension Dr. Guangliang Chen | Mathematics & Statistics, San José State University2/30 Math 253 course introduction and overview This course focuses on the statistical/machine learning task of dimension reduction, also called dimensionality reduction, which is the process of reducing the number of input variables of a data set under consideration, for the following benefits: • It reduces the running time and storage space. • Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model. • It can also clean up the data by reducing the noise. • It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D. Dr. Guangliang Chen | Mathematics & Statistics, San José State University3/30 Math 253 course introduction and overview There are two different kinds of dimension reduction approaches: • Feature selection approaches try to find a subset of the original features variables. Examples: subset selection, stepwise selection, Ridge and Lasso regression. ←− Already covered in Math 261A • Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. ←− Focus of this course Examples: principal component analysis (PCA), ISOmap, and linear discriminant analysis (LDA). Dr. Guangliang Chen | Mathematics & Statistics, San José State University4/30 Math 253 course introduction and overview Dimension reduction methods to be covered in this course: • Linear projection methods: – PCA (for unlabled data), – LDA (for labled data) • Nonlinear embedding methods: – Multidimensional scaling (MDS), ISOmap – Locally linear embedding (LLE) – Laplacian eigenmaps Dr.
    [Show full text]
  • Volume Rendering
    Volume Rendering 1.1. Introduction Rapid advances in hardware have been transforming revolutionary approaches in computer graphics into reality. One typical example is the raster graphics that took place in the seventies, when hardware innovations enabled the transition from vector graphics to raster graphics. Another example which has a similar potential is currently shaping up in the field of volume graphics. This trend is rooted in the extensive research and development effort in scientific visualization in general and in volume visualization in particular. Visualization is the usage of computer-supported, interactive, visual representations of data to amplify cognition. Scientific visualization is the visualization of physically based data. Volume visualization is a method of extracting meaningful information from volumetric datasets through the use of interactive graphics and imaging, and is concerned with the representation, manipulation, and rendering of volumetric datasets. Its objective is to provide mechanisms for peering inside volumetric datasets and to enhance the visual understanding. Traditional 3D graphics is based on surface representation. Most common form is polygon-based surfaces for which affordable special-purpose rendering hardware have been developed in the recent years. Volume graphics has the potential to greatly advance the field of 3D graphics by offering a comprehensive alternative to conventional surface representation methods. The object of this thesis is to examine the existing methods for volume visualization and to find a way of efficiently rendering scientific data with commercially available hardware, like PC’s, without requiring dedicated systems. 1.2. Volume Rendering Our display screens are composed of a two-dimensional array of pixels each representing a unit area.
    [Show full text]
  • Graph Visualization and Navigation in Information Visualization 1
    HERMAN ET AL.: GRAPH VISUALIZATION AND NAVIGATION IN INFORMATION VISUALIZATION 1 Graph Visualization and Navigation in Information Visualization: a Survey Ivan Herman, Member, IEEE CS Society, Guy Melançon, and M. Scott Marshall Abstract—This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, state–transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective. Index Terms—Information visualization, graph visualization, graph drawing, navigation, focus+context, fish–eye, clustering. involved in graph visualization: “Where am I?” “Where is the 1 Introduction file that I'm looking for?” Other familiar types of graphs lthough the visualization of graphs is the subject of this include the hierarchy illustrated in an organisational chart and Asurvey, it is not about graph drawing in general. taxonomies that portray the relations between species. Web Excellent bibliographic surveys[4],[34], books[5], or even site maps are another application of graphs as well as on–line tutorials[26] exist for graph drawing. Instead, the browsing history. In biology and chemistry, graphs are handling of graphs is considered with respect to information applied to evolutionary trees, phylogenetic trees, molecular visualization. maps, genetic maps, biochemical pathways, and protein Information visualization has become a large field and functions. Other areas of application include object–oriented “sub–fields” are beginning to emerge (see for example Card systems (class browsers), data structures (compiler data et al.[16] for a recent collection of papers from the last structures in particular), real–time systems (state–transition decade).
    [Show full text]
  • From Surface Rendering to Volume
    What is Computer Graphics? • Computational process of generating images from models and/or datasets using computers • This is called rendering (computer graphics was traditionally considered as a rendering method) • A rendering algorithm converts a geometric model and/or dataset into a picture Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK What is Computer Graphics? This process is also called scan conversion or rasterization How does Visualization fit in here? Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Computer Graphics • Computer graphics consists of : 1. Modeling (representations) 2. Rendering (display) 3. Interaction (user interfaces) 4. Animation (combination of 1-3) • Usually “computer graphics” refers to rendering Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Computer Graphics Components Department of Computer Science CSE364 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Surface Rendering • Surface representations are good and sufficient for objects that have homogeneous material distributions and/or are not translucent or transparent • Such representations are good only when object boundaries are important (in fact, only boundary geometric information is available) • Examples: furniture, mechanical objects, plant life • Applications: video games, virtual reality, computer- aided design Department of
    [Show full text]
  • An Interview with Visualization Pioneer Ben Shneiderman
    6/23/2020 The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman Jessica Hullman Follow Mar 12, 2019 · 13 min read Few people in visualization research have had careers as long and as impactful as Ben Shneiderman. We caught up with Ben over email in between his travels to get his take on visualization research, what’s worked in his career, and his advice for practitioners and researchers. Enjoy! Multiple Views: One of the main purposes of this blog is to explain to people what visualization research is to practitioners and, possibly, laypeople. How would you answer the question “what is visualization research”? Ben S: First let me define information visualization and its goals, then I can describe visualization research. Information visualization is a powerful interactive strategy for exploring data, especially when combined with statistical methods. Analysts in every field can use interactive information visualization tools for: more effective detection of faulty data, missing data, unusual distributions, and anomalies deeper and more thorough data analyses that produce profounder insights, and richer understandings that enable researchers to ask bolder questions. Like a telescope or microscope that increases your perceptual abilities, information visualization amplifies your cognitive abilities to understand complex processes so as to support better decisions. In our best
    [Show full text]
  • Scalable Exact Visualization of Isocontours in Road Networks Via Minimum-Link Paths∗
    Journal of Computational Geometry jocg.org SCALABLE EXACT VISUALIZATION OF ISOCONTOURS IN ROAD NETWORKS VIA MINIMUM-LINK PATHS∗ Moritz Baum,y Thomas Bläsius,z Andreas Gemsa,y Ignaz Rutter,yx and Franziska Wegnery Abstract. Isocontours in road networks represent the area that is reachable from a source within a given resource limit. We study the problem of computing accurate isocontours in realistic, large-scale networks. We propose isocontours represented by polygons with minimum number of segments that separate reachable and unreachable components of the network. Since the resulting problem is not known to be solvable in polynomial time, we introduce several heuristics that run in (almost) linear time and are simple enough to be implemented in practice. A key ingredient is a new practical linear-time algorithm for minimum-link paths in simple polygons. Experiments in a challenging realistic setting show excellent performance of our algorithms in practice, computing near-optimal solutions in a few milliseconds on average, even for long ranges. 1 Introduction How far can I drive my battery electric vehicle, given my position and the current state of charge? This question expresses range anxiety (the fear of getting stranded) caused by limited battery capacities and a sparse charging infrastructure. An answer in the form of a map that visualizes the reachable region helps to find charging stations in range and to overcome range anxiety. This reachable region is bounded by curves that represent points of constant energy consumption; such curves are usually called isocontours (or isolines). 2 Isocontours are typically considered in the context of functions f : R R, e.
    [Show full text]
  • Treemap Art Project
    EVERY ALGORITHM HAS ART IN IT Treemap Art Project By Ben Shneiderman Visit Exhibitions @ www.cpnas.org 2 tree-structured data as a set of nested rectangles) which has had a rippling impact on systems of data visualization since they were rst conceived in the 1990s. True innovation, by denition, never rests on accepted practices but continues to investigate by nding new In his book, “Visual Complexity: Mapping Patterns of perspectives. In this spirit, Shneiderman has created a series Information”, Manuel Lima coins the term networkism which of prints that turn our perception of treemaps on its head – an he denes as “a small but growing artistic trend, characterized eort that resonates with Lima’s idea of networkism. In the by the portrayal of gurative graph structures- illustrations of exhibition, Every AlgoRim has ART in it: Treemap Art network topologies revealing convoluted patterns of nodes and Project, Shneiderman strips his treemaps of the text labels to links.” Explaining networkism further, Lima reminds us that allow the viewer to consider their aesthetic properties thus the domains of art and science are highly intertwined and that laying bare the fundamental property that makes data complexity science is a new source of inspiration for artists and visualization eective. at is to say that the human mind designers as well as scientists and engineers. He states that processes information dierently when it is organized visually. this movement is equally motivated by the unveiling of new In so doing Shneiderman seems to daringly cross disciplinary is exhibit is a project of the knowledge domains as it is by the desire for the representation boundaries to wear the hat of the artist – something that has Cultural Programs of the National Academy of Sciences of complex systems.
    [Show full text]
  • Graphics and Visualization (GV)
    1 Graphics and Visualization (GV) 2 Computer graphics is the term commonly used to describe the computer generation and 3 manipulation of images. It is the science of enabling visual communication through computation. 4 Its uses include cartoons, film special effects, video games, medical imaging, engineering, as 5 well as scientific, information, and knowledge visualization. Traditionally, graphics at the 6 undergraduate level has focused on rendering, linear algebra, and phenomenological approaches. 7 More recently, the focus has begun to include physics, numerical integration, scalability, and 8 special-purpose hardware, In order for students to become adept at the use and generation of 9 computer graphics, many implementation-specific issues must be addressed, such as file formats, 10 hardware interfaces, and application program interfaces. These issues change rapidly, and the 11 description that follows attempts to avoid being overly prescriptive about them. The area 12 encompassed by Graphics and Visual Computing (GV) is divided into several interrelated fields: 13 • Fundamentals: Computer graphics depends on an understanding of how humans use 14 vision to perceive information and how information can be rendered on a display device. 15 Every computer scientist should have some understanding of where and how graphics can 16 be appropriately applied and the fundamental processes involved in display rendering. 17 • Modeling: Information to be displayed must be encoded in computer memory in some 18 form, often in the form of a mathematical specification of shape and form. 19 • Rendering: Rendering is the process of displaying the information contained in a model. 20 • Animation: Animation is the rendering in a manner that makes images appear to move 21 and the synthesis or acquisition of the time variations of models.
    [Show full text]
  • The Eyes Have It: a Task by Data Type Taxonomy for Information Visualizations
    The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations Ben Shneiderman Department of Computer Science, Human-Computer Interaction Laboratory, and Institute for Systems Research University of Maryland College Park, Maryland 20742 USA ben @ cs.umd.edu keys), are being pushed aside by newer notions of Abstract information gathering, seeking, or visualization and data A useful starting point for designing advanced graphical mining, warehousing, or filtering. While distinctions are user interjaces is the Visual lnformation-Seeking Mantra: subtle, the common goals reach from finding a narrow set overview first, zoom and filter, then details on demand. of items in a large collection that satisfy a well-understood But this is only a starting point in trying to understand the information need (known-item search) to developing an rich and varied set of information visualizations that have understanding of unexpected patterns within the collection been proposed in recent years. This paper offers a task by (browse) (Marchionini, 1995). data type taxonomy with seven data types (one-, two-, Exploring information collections becomes three-dimensional datu, temporal and multi-dimensional increasingly difficult as the volume grows. A page of data, and tree and network data) and seven tasks (overview, information is easy to explore, but when the information Zoom, filter, details-on-demand, relate, history, and becomes the size of a book, or library, or even larger, it extracts). may be difficult to locate known items or to browse to gain an overview, Designers are just discovering how to use the rapid and Everything points to the conclusion that high resolution color displays to present large amounts of the phrase 'the language of art' is more information in orderly and user-controlled ways.
    [Show full text]
  • Tasks, Techniques, and Tools for Genomic Data Visualization
    Tasks, Techniques, and Tools for Genomic Data Visualization Sabrina Nusrat1;∗ Theresa Harbig1;∗ and Nils Gehlenborg1 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; ∗Authors contributed equally to this work Data Taxonomy Visualization Taxonomy Task Taxonomy Feature Sets Coordinate System Tracks View Configurations Tasks Type Interconnection Layout Partition Abstraction Arrangement Encoding Alignment Views Scales Foci Search Query Lp Lookup I Identify Lt Locate C Compare One None B Browse S Summarize E Explore Sparse Within Many Mapping Feature Sets Positions Between B S C Contiguous S Lt E Lp I S E Figure 1: Data taxonomy, visualization taxonomy, and task taxonomy for genomic visualizations. The data taxonomy describes how different genomic data types can be encoded as feature sets. A genomic visualization contains one or multiple coordinate systems applying a specific layout, partition, abstraction and arrangement (in case of multiple axes) of sequence coordinates. Feature sets are encoded as tracks and placed on the coordinate systems. Multiple tracks can either be aligned by stacking or overlaying. A visualization can consist of one or multiple views, each containing a set of aligned tracks. Multiple views can show data on one or multiple scales and foci. The task taxonomy explains how different search and query tasks operate on genomic visualizations. Abstract Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data-driven and many studies lack well- defined hypotheses.
    [Show full text]
  • Christopher L. North – Curriculum Vitae (Updated Sept 2014)
    Christopher L. North – Curriculum Vitae (updated Sept 2014) Department of Computer Science (540) 231-2458 114 McBryde Hall (540) 231-9218 fax Virginia Tech north @ vt . edu Blacksburg, VA 24061-0106 http://www.cs.vt.edu/~north/ Google Scholar: • http://scholar.google.com/citations?user=yBZ7vtkAAAAJ • h-index = 35 Short Bio: Dr. Chris North is a Professor of Computer Science at Virginia Tech. He is Associate Director of the Discovery Analytics Center, and leads the Visual Analytics research group. He is principle architect of the GigaPixel Display Laboratory, one of the most advanced display and interaction facilities in the world. He also participates in the Center for Human-Computer Interaction, and the Hume Center for National Security, and is a member of the DHS supported VACCINE Visual Analytics Center of Excellence. He was awarded Faculty Fellow of the College of Engineering in 2007, and the Dean’s Award for Research Excellence in 2014. He earned his Ph.D. at the University of Maryland, College Park, in 2000. He has served as General Co-Chair of IEEE VisWeek 2009, and as Papers Chair of the IEEE Information Visualization (InfoVis) and IEEE Visual Analytics Science and Technology (VAST) Conferences. He has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics (TVCG), the Information Visualization journal, and Foundations and Trends in HCI. He has been awarded over $6M in grants, co-authored over 100 peer-reviewed publications, and delivered 3 keynote addresses at symposia in the field. He has graduated 8 Ph.D. and 14 M.S. thesis students, 4 receiving outstanding research awards at Virginia Tech, and advised over 70 undergraduate research students including several award winners at Virginia Tech’s annual undergraduate research symposium.
    [Show full text]