Communicating science: the elevator speech

In a previous post, I described my experience at a workshop (organized by the Rutgers Graduate School-New Brunswick) on communicating science.  I described the importance of preparing descriptions of your work for a spectrum of likely audiences – having at least some idea of what aspects of your work to emphasize to different audiences and what language or ideas to use are critical.  However, in addition to these more customized versions, having a more generic but highly-polished description of your research that you can recite from memory at any time is probably worth having.  This is often known as the “elevator speech,” since it’s supposed to be something simple and short enough that you can say it during the time you’d spend with a stranger in an elevator.

I’ve had a murky version of this for a while, but it was largely a vague set of examples and analogies I liked to use when describing my research to a friend or family member rather than a well-crafted summary.  But the workshop motivated me to finally develop a better version, so here is my latest attempt:

Every cell in your body contains thousands of different kinds of molecules, stuffed into a very small space and interacting with each other in complex ways.  How does this mess of molecules ultimately do all things that cells do, such as making new cells, extracting energy from food, and transporting nutrients?  And how did the precise interactions of all these molecules develop over millions of years of evolution?  This knowledge is important both for treating human diseases in which these cellular functions go wrong (e.g., runaway cancer cell growth), as well as engineering microorganisms to perform useful jobs, such as synthesizing biofuels with bacteria or making better beer with yeast.  My research uses mathematical models and computational techniques to understand how natural selection changes these molecules and their interactions over time.  We want to use this both to understand how organisms naturally evolved in the past and to predict how they might evolve in the future.

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