VizUM 2016

The VizUM 3rd Annual Symposium will be held on Thursday, November 10, 2016, from 4:00-7:00 PM at the University of Miami Newman Alumni Center, 6200 San Amaro Drive, Coral Gables, FL 33146.  Our guest speakers are Colin Ware and Martin Krzywinski.

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COLIN WARE

Center for Coastal and Ocean Mapping   |  University of New Hampshire

Colin Ware specializes in applying theories of perception to the design visualizations. He has advanced degrees in both computer science (MMath, Waterloo) and in the psychology of perception (PhD,Toronto). He has published over 160 scientific articles in the fields of data visualization and human-computer interaction. His book Information Visualization: Perception for Design is now in its third edition.  His book, Visual Thinking for Design, appeared in 2008. Ware also likes to build practical visualization systems. Fledermaus, a commercial 3D geospatial visualization system widely used in oceanography, was developed from his initial prototypes. His trackPlot software is being used by marine mammal scientists and his flowVis2D software is serving images on NOAA websites. Ware is Director of the Data Visualization Research Lab which is part of the Center for Coastal and Ocean Mapping at the University of New Hampshire.

Visual Thinking about Scientific Data:  The Cognitive Processes Whereby we Gain Knowledge

Visual thinking is a process.  We do not just take in information “at a glance”; rather, what we perceive depends on what information we are seeking and the visual system is tuned accordingly.  This is especially true when we are doing science. The scientist uses visualizations as a tool to confirm and refute hypothesis, present results, gain new insights into data, and occasionally make new discoveries.  Each of these entails a different thinking processes. In this talk we will trace how the thread of cognition depends on the task as well as the perceptual issues that determine if a visualization is successful. Examples will range from weather displays and tools to help scientists understand the tracks of sea lions as they forage off the coast of California.

MARTIN KRZYWINSKI

Canada’s Michael Smith Genome Sciences Centre  |  British Columbia Cancer Agency

Martin Krzywinski is known for his work in bioinformatics and data visualization. He created the Circos graph to display genomic data sets in a way that revealed their inner structure and served as a visually stunning emblem of the new field. His information graphics have appeared in the New York Times, Wired, Scientific American and covers of numerous books and scientific journals. Krzywinski’s work has set a new standard for the presentation of scientific results and established design as a tool of discovery in the research process itself.

Fitting Big Science on a Small Page

An exhaustive explanation is an exhausting one. My own goal is to leave the audience energized and motivated to continue to conversation, which should flow naturally beyond the scope of the design. They can always ask for more but they cannot ask for less. Assuming that a design will act as a first explanation motivates me for the need to distinguish essentials from ever-present modifiers and merely interesting tangents. While everything may indeed be important, initially some things are more important than others. Classifying aspects of the science this way always feels risky—how do I know that I know enough to justify leaving things out? I will use examples of my designs to create a first explanation of how to approach creating first explanations. What is the right amount of design detail that shows that we share 99% of our DNA with chimps—and does this statement hinder our understanding of evolution? How can I communicate the relationships between diseases and genetic modification specific to certain tissues—is there structure in the data that can inform how it is shown? If the composition of household bacteria vary based on the occupants’ gender and presence of dogs and cats, how do I present all the possible ratio of gender and animals—does the size of the dataset belie the simplicity of the story behind it?