TITLE: Workshop 6: The Next Generation of Scientists in the Workforce
AUTHOR: Eugene Wallingford
DATE: November 10, 2008 7:31 PM
[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
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:
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
North by Northwest.)
How does computational thinking help the company do
more better and faster? By...
- 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
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
106 cells/patient X 50 patients/experiment
X 20 challenges X 20 markers
- ... letting scientists spend more time doing what
- ... eliminating low-value-add transactional
activities in the business process.
- ... boosting the speed and scalability of their
→ 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
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
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.
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
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
tasks. There is room for a lot of great algorithm
Finally, Spellmeyer weighed in on a hot topic from the
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.
- 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.