TITLE: Workshop 6: The Next Generation of Scientists in the Workforce AUTHOR: Eugene Wallingford DATE: November 10, 2008 7:31 PM DESC: ----- BODY:

[A transcript of the SECANT 2008 workshop: Table of Contents]

The last session of an eventful workshop consisted of two people. One was a last minute sub for a science speaker who had to pull out. The sub, from Microsoft Research, didn't add much science content, but did say something I wish undergrads would pick up on. What do all companies look for these days? Short ramp-up time, self-starters. These boil down to curiosity and initiative. The second speaker gave the sort of industry report I so enjoyed last year. David Spellmeyer, a Purdue computer science and chemistry grad, is CTO and CIO at Nodality. He titled his talk, "Computational Thinking as a Competitive Advantage in Industry". I love that title! because I love the ways computing confers a competitive advantage over companies that don't get it yet. The downside of Spellmeyer talking about his own company's competitive advantage: he can't post his slides. Spellmeyer did tell us a bit about his company's science at various points in his story. Nodality works on patient-specific classification of disease and response to therapies. At least part of that involves evaluating phosphoprotein-signaling networks. (I hope that doesn't give too much away.) He looks for computational thinking skills in all of the scientists Nodality hires. His CT wish list included items familiar and surprising:

familiarity with the complexity of computing
exposure to programming languages
analytical methods for experimental studies
familiarity with the technology and inner workings of the computer, especially database

These skills give competitive advantage to his company -- and also to the individual! The company is able to do more better and faster. The individual has better judgment across a wider range of problems. These advantages intersect at a point where computational thinking demystifies the computer, computer systems, and programming. Understanding even a little about computers and programs helps to dispel myth of the perfect computer and the perfect computer system. Those myths create frustrations that grow into more. (Spellmeyer used another image to drive this point home: Hitchcock's North by Northwest.) How does computational thinking help the company do more better and faster? By...

... letting scientists spend more time doing what they love.
... eliminating low-value-add transactional activities in the business process.
... boosting the speed and scalability of their systems.

Notice that these advantages range from the scientific to business process to the technical. It's not only about techies sitting in front of monitors. On the scientific side of the equation, Nodality has a data problem. A robust assay produces a flood of data: 10⁶ cells/patient X 50 patients/experiment X 20 challenges X 20 markers
→ 10¹⁰ data points per experiment Thereafter followed a lot of detail that I couldn't follow in real time, which is probably just as well. There is a reason that Spellmeyer can't post his slides... How do they eliminate low-value-added transactional activities?

Talk to customers.
Find patterns of practice.
Propose computational tools to improve practice.
Use an agile approach to gather requirements, design a system, field, get feedback, and iterate in short cycles.

Computational thinking enables scientists and techies to think of their experiments, and how to set them up, in different ways. For example, they might conceive of a way to set up a cytometer differently. They also think differently about experiment analysis and inventory management. As Spellmeyer wrapped up, he he included a few snippets to motivate his ideas and the scale of the problems that he and his company face. He quoted Margaret Wheatley as saying that all science is a metaphor, a description of a reality we can never fully know. As a pragmatist, this is something I believe almost from the outset. He also said that in business, learning occurs naturally through normal interactions in work practices. Not in classes. "Context, community, and content" are the triumvirate that drives all they do. For this reason, his company puts a lot of effort into its community software tools. The problem ultimately comes down to an issue at the intersection of combinatorics, pragmatics, and even ethics. We can make billions of unique molecules. Which ones should we make? We need to consider molecules similar enough to ones we understand but dissimilar enough to offer hope of a new result. This leads to a question of similarity and dissimilarity, one of those AI-complete tasks. There is room for a lot of great algorithm exploration here. Finally, Spellmeyer weighed in on a hot topic from the previous session: Excel is a basic tool in his company. The business guys have developed an extremely complex business model, and all of their work is in Excel. But it's not just a work horse on the business side; scientists use Excel to transform data. He is happy to find scientists and techies alike who know how to use Excel at full strength. -----