Peter Lee Head, Computer Science Department Carnegie Mellon University

The dark laboratory?

DataComputingPh.D.s Crowds

Predicted alignment for known B-helices on cross-family validation [Carbonell, et al] [Tom Mitchell, Marcel Just]

Fluid dynamics model reduction [AdrienTreuille, et al] “Universe in a box” [TizianadiMatteo, et al]

30M reCAPTCHAs are being solved per day, allowing the equivalent of 125 books to be digitized daily. Some people solve them simply to help digitize books. Over 750M different people have solved a CAPTCHA. Luis von Ahn, et al.

The deluge of data makes data-intensive computing mandatory for many future scientific endeavors Future students (and, ultimately, scientists) will find it natural for the data (and computing) to be “in the cloud” There are many big technology problems to be solved But there are also organizational and cultural gaps

A wide field-of-view telescope with 3.2Gpx camera, to be operational in 2014 Will provide full-sky survey (15sec exposure) every three nights In support of basic astronomy research, as well as change detection (eg, for asteroids) On the order of 16TB of image data per day A major focus on the hardware and pipes

In the 2014 time frame, such data storage requirements may not be extraordinary But deriving knowledge from such data streams efficiently will be hard Astronomers don’t yet know what questions they will want to ask It’s all about the software, and in this case the software is research

Virtually all major research instruments of the future will be software-driven, but with big lead times and unknowns Funding programs need to address and tolerate this Who gets to read? Who gets to write? Who gets to write code? What are the ownership, allocation, and trust models? Are there reward structures for researchers to develop the software architectures, APIs, languages, and algorithms?

Microsoft Research Faculty Summit 2008