Tamara Munzner


Research Classification

Research Interests

information visualization
visual analytics
data science

Relevant Degree Programs



Complete these steps before you reach out to a faculty member!

Check requirements
  • Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
  • Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Requirements" or on the program website.
Focus your search
  • Identify specific faculty members who are conducting research in your specific area of interest.
  • Establish that your research interests align with the faculty member’s research interests.
    • Read up on the faculty members in the program and the research being conducted in the department.
    • Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
Make a good impression
  • Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
    • Do not send non-specific, mass emails to everyone in the department hoping for a match.
    • Address the faculty members by name. Your contact should be genuine rather than generic.
  • Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
  • Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to peek someone’s interest.
  • Demonstrate that you are familiar with their research:
    • Convey the specific ways you are a good fit for the program.
    • Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
  • Be enthusiastic, but don’t overdo it.
Attend an information session

G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.


Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round

Graduate Student Supervision

Doctoral Student Supervision (2008-2018)
Why visualization? : task abstraction for analysis and design (2016)

Why do people visualize data? People visualize data either to consume or produce information relevant to a domain-specific problem or interest. Visualization design and evaluation involves a mapping between domain problems or interests and appropriate visual encoding and interaction design choices. This mapping translates a domain-specific situation into abstract visualization tasks, which allows for succinct descriptions of tasks and task sequences in terms of why data is visualized, what dependencies a task might have in terms of input and output, and how the task is supported in terms of visual encoding and interaction design choices. Describing tasks in this way facilitates the comparison and cross-pollination of visualization design choices across application domains; the mapping also applies in reverse, whenever visualization researchers aim to contextualize novel visualization techniques. In this dissertation, we present multiple instances of visualization task abstraction, each integrating our proposed typology of abstract visualization tasks. We apply this typology as an analysis tool in an interview study of individuals who visualize dimensionally reduced data in different application domains, in a post-deployment field study evaluation of a visual analysis tool in the domain of investigative journalism, and in a visualization design study in the domain of energy management. In the interview study, we draw upon and demonstrate the descriptive power of our typology to classify five task sequences relating to visualizing dimensionally reduced data. This classification is intended to inform the design of new tools and techniques for visualizing this form of data. In the field study, we draw upon and demonstrate the descriptive and evaluative power of our typology to evaluate Overview, a visualization tool for investigating large text document collections. After analyzing its adoption by investigative journalists, we characterize two abstract tasks relating to document mining and present seven lessons relating to the design of visualization tools for document data. In the design study, we demonstrate the descriptive, evaluative, and generative power of our typology and identify matches and mismatches between visualization design choices and three abstract tasks relating to time series data. Finally, we reflect upon the impact of our task typology.


Practical considerations for Dimensionality Reduction : user guidance, costly distances, and document data (2013)

In this thesis, we explore ways to make practical extensions to Dimensionality Reduction, or DR algorithms with the goal of addressing challenging, real-world cases. The first case we consider is that of how to provide guidance to those users employing DR methods in their data analysis. We specifically target users who are not experts in the mathematical concepts behind DR algorithms. We first identify two levels of guidance: global and local. Global user guidance helps non-experts select and arrange a sequence of analysis algorithms. Local user guidance helps users select appropriate algorithm parameter choices and interpret algorithm output. We then present a software system, DimStiller, that incorporates both types of guidance, validating it on several use-cases. The second case we consider is that of using DR to analyze datasets consisting of documents. In order to modify DR algorithms to handle document datasets effectively, we first analyze the geometric structure of document datasets. Our analysis describes the ways document datasets differ from other kinds of datasets. We then leverage these geometric properties for speed and quality by incorporating ideas from text querying into DR and other algorithms for data analysis. We then present the Overview prototype, a proof-of-concept document analysis system. Overview synthesizes both the goals of designing systems for data analysts who are DR novices, and performing DR on document data. The third case we consider is that of costly distance functions, or when the method used to derive the true proximity between two data points is computationally expensive. Using standard approaches to DR in this important use-case can result in either unnecessarily protracted runtimes or long periods of user monitoring. To address the case of costly distances, we develop an algorithm framework, Glint, which efficiently manages the number of distance function calculations for the Multidimensional Scaling class of DR algorithms. We then show that Glint implementations of Multidimensional Scaling algorithms achieve substantial speed improvements or remove the need for human monitoring.


Feature-based graph visualization (2008)

A graph consists of a set and a binary relation on that set. Each elementof the set is a node of the graph, while each element of the binary relationis an edge of the graph that encodes a relationship between two nodes.Graph are pervasive in many areas of science, engineering, and the socialsciences: servers on the Internet are connected, proteins interact in largebiological systems, social networks encode the relationships between people,and functions call each other in a program. In these domains, the graphscan become very large, consisting of hundreds of thousands of nodes andmillions of edges.Graph drawing approaches endeavour to place these nodes in two orthree-dimensional space with the intention of fostering an understandingof the binary relation by a human being examining the image. However,many of these approaches to drawing do not exploit higher-level structuresin the graph beyond the nodes and edges. Frequently, these structures canbe exploited for drawing. As an example, consider a large computer networkwhere nodes are servers and edges are connections between those servers.If a user would like understand how servers at UBC connect to the rest ofthe network, a drawing that accentuates the set of nodes representing thoseservers may be more helpful than an approach where all nodes are drawn inthe same way. In a feature-based approach, features are subgraphs exploitedfor the purposes of drawing. We endeavour to depict not only the binaryrelation, but the high-level relationships between features.This thesis extensively explores a feature-based approach to graph visualization and demonstrates the viability of tools that aid in the visualization of large graphs. Our contributions lie in presenting and evaluatingnovel techniques and algorithms for graph visualization. We implement fivesystems in order to empirically evaluate these techniques and algorithms,comparing them to previous approaches.


Visual exploratory analysis of large data sets : evaluation and application (2008)

Large data sets are difficult to analyze. Visualization has been proposed to assist exploratory data analysis (EDA) as our visual systems can process signals inparallel to quickly detect patterns. Nonetheless, designing an effective visualanalytic tool remains a challenge.This challenge is partly due to our incomplete understanding of how commonvisualization techniques are used by human operators during analyses, either inlaboratory settings or in the workplace.This thesis aims to further understand how visualizations can be used to support EDA. More specifically, we studied techniques that display multiple levels of visual information resolutions (VIRs) for analyses using a range of methods.The first study is a summary synthesis conducted to obtain a snapshot ofknowledge in multiple-VIR use and to identify research questions for the thesis:(1) low-VIR use and creation; (2) spatial arrangements of VIRs. The next twostudies are laboratory studies to investigate the visual memory cost of imagetransformations frequently used to create low-VIR displays and overview usewith single-level data displayed in multiple-VIR interfaces.For a more well-rounded evaluation, we needed to study these techniques inecologically-valid settings. We therefore selected the application domain of websession log analysis and applied our knowledge from our first three evaluationsto build a tool called Session Viewer. Taking the multiple coordinated viewand overview + detail approaches, Session Viewer displays multiple levels ofweb session log data and multiple views of session populations to facilitate dataanalysis from the high-level statistical to the low-level detailed session analysisapproaches.Our fourth and last study for this thesis is a field evaluation conducted atGoogle Inc. with seven session analysts using Session Viewer to analyze theirown data with their own tasks. Study observations suggested that displayingweb session logs at multiple levels using the overview + detail technique helped bridge between high-level statistical and low-level detailed session analyses, andthe simultaneous display of multiple session populations at all data levels usingmultiple views allowed quick comparisons between session populations. We alsoidentified design and deployment considerations to meet the needs of diversedata sources and analysis styles.


Master's Student Supervision (2010-2017)
A search set model of path tracing in graphs (2014)

Path tracing is a common task in many real world uses of graphs that display networks of relationships. Despite previous work in the evaluation of how factors, such as edge-edge crossings, impact the readability of graph layouts, what makes one path-tracing task more difficult than another is not well understood.To address this question we conducted an observational user study with 12 participants completing a path-tracing task. Our extensive qualitative analysis of the study data led to a detailed characterization of common path-tracing behaviours. We then created a predictive model of the paths that users are most likely to search, which we name the search set, based on the behaviours we observed. To validate our predictive behavioural model, and to demonstrate how the search set could be used, we conducted a careful comparison of graph readability factors through a hierarchical multiple regression analysis.


Variant view : visualizing sequence variants in their gene context (2013)

Scientists use DNA sequence differences between an individual's genome and a standard reference genome to study the genetic basis of disease. Such differences are called sequence variants, and determining their impact in the cell is difficult because it requires reasoning about both the type and location of the variant across several levels of biological context. In this design study, we worked with four analysts to design a visualization tool supporting variant impact assessment for three different tasks. We contribute data and task abstractions for the problem of variant impact assessment, and the carefully justified design and implementation of the Variant View tool. Variant View features an information-dense visual encoding that provides maximal information at the overview level, in contrast to the extensive navigation required by currently-prevalent genome browsers. We provide initial evidence that the tool simplified and accelerated workflows for these three tasks through three case studies. Finally, we reflect on the lessons learned in creating and refining data and task abstractions that allow for concise overviews of sprawling information spaces that can reduce or remove the need for the memory-intensive use of navigation.


Current Students & Alumni

This is a small sample of students and/or alumni that have been supervised by this researcher. It is not meant as a comprehensive list.

If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.