Discussion by Evan Golub

� Was a graduate student in Harvard's School of

Engineering and Applied Sciences at the time.

� Degree in physics with interests in visualization

and perception.

� Currently a Postdoctoral Research Fellow in the

Computer Science Department at the University of

British Columbia & Associate in Computer Science

in the School of Engineering & Applied Sciences at

Harvard University

� An astronomer was searching for way to look at astronomical data and make sense of it.� Borkin suggested applying techniques from biomedical

imaging to astronomical data.

� The astronomer was able to realize new things as a result of the visualization.

� Heart disease diagnostics CT scan data.� Borkin decided to use tree diagrams from her

astronomy work and shades of red from her infovizcollaborators.

� Increased identification of risk regions from 39% to 91%.

� I would have liked to hear (briefly) how the doctors

and astronomers do at the game. Since I am not a

specialist in either area, I don’t know if it’s an easy

or hard game.

� Is there something in computing that CS practitioners

could easily tell the difference between but non-CS

people wouldn’t?

▪ Complexity of code?

▪ Real or fake programming languages?

� An astronomer was searching for way to look at astronomical data and make sense of it.� Borkin suggested applying techniques from biomedical

imaging to astronomical data.

� The astronomer was able to realize new things as a result of the visualization.

� Heart disease diagnostics CT scan data.� Borkin decided to use tree diagrams from her

astronomy work and shades of red from her infovizcollaborators.

� Increased identification of risk regions from 39% to 91%.

� Interesting to learn the (or at least an) early origin

of tree-based visualizations going back to Darwin.

� One of the visualizations might have been a

TreeMap (invented locally by Ben Shneiderman)

and used on sites like the MarketWatch “Map of

the Market” (http://www.marketwatch.com/tools/stockresearch/marketmap)

and Newsmap (http://newsmap.jp)

� They’ve even been used as art (see the 3rd floor of CSIC).

� The way MRI scanners and radio telescopes gather

data are similar in how they both go “slice by slice”

as they look through the subject of interest.

� The visualization techniques that the biomedical

world has been using can also be useful to the

astronomical world.

� 3D visualization toolkits that doctors use can also

be utilized by astronomers.

� By taking CT data about a heart and using a visualization

technique typically applied to nebula data, and not using a

rainbow color map, doctors who only find 39% of high-risk

regions with the first find 91% of the high-risk regions.

http://www.color-blindness.com/2012/10/22/ishiharas-test-for-colour-deficiency38-plates-edition/

� The human eye has approximately 120 million rods but

fewer than 10 million cones (between 6 and 8 million).

� Brightness and color are detected respectively by the rods

and cones on the retina.

� There are three types of cones, each with a sensitivity to

light of a different wavelength (short, medium, long).

They are essentially red, green, and blue.

� We have around twice as many red cones as green, and

around eight times as many green as blue.

http://hyperphysics.phy-astr.gsu.edu/hbase/vision/rodcone.htmlhttp://askabiologist.asu.edu/rods-and-cones

� It wasn’t clear whether the 3D image was the type of visualization that was commonly used for this application, since it was stated that it originally was used for studying DNA.

� It wasn’t clear how they know the 100% level of high-risk regions to compare against.

� I suggest explaining how the “100%” answer is found and that the end of the example showing the improvements offered by the red-scale 2D tree image should have compared results to a red-scale version of the 3D image for a more complete end-to-end comparison.

� In “The Structure of Scientific Revolutions” (1962) Thomas Kuhn discusses examples of outsiders to a discipline and paradigm shifts.� A classic historical example is how Watson and Crick

came from other sciences to present the idea of a double-helix for DNA’s structure.

� In “The Medici Effect: What Elephants and Epidemics Can Teach Us About Innovation” (2004) Frans Johansson talks about the “intersection” as a place where diverse ideas collide and lead to innovation.

� What area insiders from outside CS might be able

to contribute to our field in the future and what

outside fields could benefit from a CS insider?