Tableau Software
Director, Product Management
Tableau Software
Principal Technical Advisor
Tableau Software 2012 - Jan 2018
Senior Research Scientist
Stanford University Sep 2007 - 2012
Research Assistant
386 Gates Sep 2010 - Jan 2011
Founder-Esque
Education:
Stanford University 2006 - 2013
Doctorates, Doctor of Philosophy, Computer Science
Brigham Young University 2003 - 2005
Master of Science, Masters, Computer Science
Brigham Young University 1997 - 2003
Bachelors, Bachelor of Science, Computer Science
Comr.se Seattle, WA Apr 2014 to Jan 2015 Principal Data ScientistPlayhaven San Francisco, CA 2013 to 2013 Data ScientistBadgeville Menlo Park, CA 2012 to Jan 2012 Technical Support EngineerPlayhaven San Francisco, CA 2012 to 2012 Technical Support EngineerUnisys Technical Services Rochester, NY Jun 2011 to Jun 2011Rochester Institute of Technology
Jun 2008 to Aug 2008Rochester Institute of Technology Rochester, NY Research Assistant
Education:
Rochester Institute of Technology Rochester, NY 2009 to 2011 PhD in Computing and Information SciencesRochester Institute of Technology Rochester, NY May 2009 B.S. in Applied Mathematics
Us Patents
Interactive Visualization For Generating Ensemble Classifiers
Bongshin Lee - Issaquah WA, US Ashish Kapoor - Kirkland WA, US Desney S. Tan - Kirkland WA, US Justin Talbot - Stanford CA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 15/18 G06F 17/00 G06N 5/04
US Classification:
706 61
Abstract:
A real-time visual feedback ensemble classifier generator and method for interactively generating an optimal ensemble classifier using a user interface. Embodiments of the real-time visual feedback ensemble classifier generator and method use a weight adjustment operation and a partitioning operation in the interactive generation process. In addition, the generator and method include a user interface that provides real-time visual feedback to a user so that the user can see how the weight adjustment and partitioning operation affect the overall accuracy of the ensemble classifier. Using the user interface and the interactive controls available on the user interface, a user can iteratively use one or both of the weigh adjustment operation and partitioning operation to generate an optimized ensemble classifier.
Methods And User Interfaces For Generating Level Of Detail Calculations For Data Visualizations
- Seattle WA, US Allan Folting - Woodinville WA, US Daniel Philip Cory - Seattle WA, US Justin Talbot - Seattle WA, US Lauren Christina Lum - West Lafayette IN, US Elaine Weatherfield Sulc - Seattle WA, US Susan Denise Doan - Brier WA, US
International Classification:
G06F 3/0486 G06F 3/04847
Abstract:
A computing device displays a data visualization interface that includes a shelf region and a schema information region. The device receives user input to select a measure data field and a dimension data field from the schema information region. The device generates a custom calculation that groups data values of the dimension data field according to respective distinct data values of the dimension data field and aggregates data values of the measure data field for each of the distinct data values of the dimension data field. The device stores the custom calculation as a new selectable data field and displays the new selectable data field in the schema information region. The device receives user selection of the new selectable data field from the schema information region and placement of the new selectable data field in the shelf region. The device generates and displays a data visualization.
User Interface For Generating Data Visualizations That Use Table Calculations
- Seattle WA, US Christopher Richard Stolte - seattle WA, US Jock Douglas Mackinlay - Clyde Hill WA, US Ross Thomas Bunker - Seattle WA, US Bora Beran - Santa Fe NM, US Justin Talbot - Seattle WA, US
An electronic device displays a chart, which includes a first set of visual marks that represent values derived from a set of data. The device concurrently displays a chart-calculations-options area. The chart-calculations-options area displays a first plurality of options for type of calculation. The device detects user selection of (i) a first option, from the first plurality of options, for type of calculation and (ii) a second option corresponding to a parameter for the selected first option. In response to detecting the user selection, the device visually distinguishes visual marks in a second set of visual marks in the chart. The second set of visual marks is distinct from the first set of visual marks and corresponds to the first option for type of calculation and the second option corresponding to the parameter for the selected first option.
Systems And Methods For Using Displayed Data Marks In A Dynamic Data Visualization Interface
- Seattle WA, US Christopher Richard Stolte - Seattle WA, US Jock Douglas Mackinlay - Seattle WA, US Robin Stewart - Seattle WA, US Bora Beran - Bothell WA, US Justin Talbot - Seattle WA, US Marc Rueter - Seattle WA, US
A method displays a chart that includes visual marks representing a data set, displayed according to contents of displayed shelf regions, which determine characteristics of the chart. The method detects selection of a plurality of visual marks, and visually emphasizes the selected plurality of visual marks. The method also detects a first input on the selected marks, and displays a moveable icon corresponding to the selected visual marks while maintaining display of the visual marks. The method detects a second input on the moveable icon, and moves the moveable icon over a first shelf region. Upon ceasing to detect the input, the method updates the content of the first shelf region based on the selected visual marks, and updates the chart in accordance with updated content of the first shelf region, including applying the characteristic of the first shelf to the selected visual marks distinct from the unselected visual marks.
Utilizing Appropriate Measure Aggregation For Generating Data Visualizations Of Multi-Fact Datasets
A computer receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with either data fields (e.g., dimension and/or measures) or filters. The computer obtains a data model encoding the data source as a tree of related logical tables. Each logical table includes logical fields, each of which corresponds to either a data field or a calculation that spans logical tables. The computer generates a dimension subquery for the dimensions and the filters. The computer also generates, for each measure, an aggregated measure subquery grouped by the dimensions. The computer forms a final query by joining the dimension subquery to each of the aggregated measure subqueries. The computer subsequently executes the final query and displays a data visualization according to the results of the final query.
User Interface For Generating Data Visualizations That Use Table Calculations
- Seattle WA, US Christopher Richard STOLTE - Seattle WA, US Jock Douglas MACKINLAY - Clyde Hill WA, US Ross Thomas BUNKER - Seattle WA, US Bora BERAN - Santa Fe NM, US Justin TALBOT - Seattle WA, US
International Classification:
G06F 16/248 G06F 3/0484 G06F 3/0482 G06T 11/20
Abstract:
An electronic device displays a chart, which includes visual marks that represent calculated values derived from a set of data, and panes formed by intersection of rows and columns. A current focus is on a first visual mark at a first position. The device concurrently displays a chart-calculations-options area, which includes display options for the type of calculation and the type of data partitioning. Visual marks in a first set of visual marks are visually distinguished from other visual marks. The first set of visual marks includes the first visual mark, and corresponds to a first selected option for the type of calculation and a first selected option for the type of data partitioning. The device detects an input that selects a second option for the type of data partitioning. In response, the device visually distinguishes visual marks in a second set of visual marks in accordance with the selection.
Systems And Methods For Using Analytic Objects In A Dynamic Data Visualization Interface
- Seattle WA, US Christopher Richard Stolte - Seattle WA, US Jock Douglas MacKinlay - Clyde Hill WA, US Robin Stewart - Seattle WA, US Bora Beran - Santa Fe NM, US Justin Talbot - Seattle WA, US Marc Rueter - Bothell WA, US
International Classification:
G06F 3/0486 G06F 3/0481 G06F 3/0482
Abstract:
A method displays a chart including visual marks representing a dataset. The chart is displayed according to contents of multiple shelf regions. The shelf regions determine characteristics of the chart. The method generates a visual analytic object by applying an analytic operation to the set of data represented by the visual marks, and displays the visual analytic object superimposed over the chart. The method detects user input to select the displayed visual analytic object and place an icon representing the visual analytic object onto a first shelf region. In response, the method updates the content of the first shelf region to associate it with the analytic operation corresponding to the visual analytic object and updates the chart according to the updated content of the first shelf region. This includes updating the chart based on values of the dataset represented by the visual marks relative to the analytic operation.
Data Visualization User Interface With Summary Popup That Includes Interactive Objects
A method displays a first data visualization according to user placement of data fields in shelves of a user interface. Each shelf specifies a property of the data visualization and the data visualization includes visual data marks corresponding to data values for data fields in a dataset. A user selects a subset of the visual data marks. A popup summary is displayed that includes data value distributions for several data fields. In the popup summary, a second user input is detected corresponding to a first data field whose data value distribution is displayed in the popup summary. In response, the method displays a moveable icon corresponding to the first data field. The method detects a third user input to place the interactive moveable icon in a shelf in the user interface. In response, a second data visualization is displayed according to placement of data fields, including the first data field.
Justin Talbot (1996-2000), Sam Anderson (2003-2007), Davon Robinson (2005-2009), Daniel Tucker (1995-1999), Bridgit Lacy (1990-1994), Danielle Belle (1989-1993)
Youtube
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