George Mason University
George Mason University Mason
George Mason University

Daniel Carr

Professor, Statistics

Contact Information MORE LESS

  • 703-993-1671 (office)
  • 4A7
  • Nguyen Engineering Building, 1711
    4400 University Drive
    Fairfax, VA 22030

staff menu:


Crystalizing statistical context.

Advancements in computing and computational data mining have resulted in the ability to garner much larger and diverse statistics than were previously available. This massive influx of data availability quickly becomes unwieldy without the use of visual shorthand. Daniel Carr has developed numerous techniques and formations for better capturing and representing quantitative data in a visual medium. By using data visualization techniques, analysts and researchers, such as those for the National Institutes of Health, are able to gather context and formulate theorems more efficiently. For instance, by examining a map of a region effected by an outbreak of E. Coli illness superimposed with distribution routes of produce, environmental factors, age demographics, etc., epidemiologists are better equipped to zero in on the source of infection.

In his 25 year history at George Mason University, Carr has taught a variety of classes in statistics graphic design and data exploration. He has served as dissertation director for more that 10 graduated PhD students, and co-authored the book, “Visualizing Data with Micromaps,” with Linda Pickle in 2010.


PhD, Statistics, University of Wisconsin-Madison (1976)

MS, Statistics, Oregon State University (1972)

MEd, Counselling , Idaho Stat University (1972)

BA, Mathematics, Whitman College (1968)

More Information

Faculty Rank: Professor


2014 - 2015 : Uncertainty in Spatial Date: Identification Visualization and Utilization. Funded by University of Texas at Dallas.

2008 - 2010 : Curriculum Design in Data Sciences. Funded by National Science Foundation.

2005 - 2008 : Visual Analytic Tools for Networks and Graphs with Attention Given to Data Synthesis and Evaluation. Funded by Office of Naval Research.

Research Interests

Data mining 
Statistical graphics
Knowledge visualization
Data exploration
Analysis of massive data sets