A Week At Work (6)

Friday.

Let's talk for a minute about generational collaboration in research.

This morning, before going back to coding, I met with two younger research scientists and their boss, a senior professor. They do ecological systems modeling in the Department of Wildlife and Fisheries, one floor down from my office. One of their projects tries to understand the seasonal movement of the Invasive Sugarcane Aphid, a bug that damages crops, with the goal of creating forecasts and warnings for farmers who can then apply pesticides more targeted. Their work is not too different from what we do, so they were interested in learning about what we do. We had a wonderful, three hour long conversation about our project and theirs, and only stopped because, despite the jaffa cakes they brought, we eventually got hungry for lunch.

The way the three described their work made me think about how different generations often approach problems differently. The two researchers are much younger than their boss and probably grew up in a time when computers were ubiquitous in colleges and maybe even schools. They know who to code, run models, and build web interfaces for users to get information over the internet. The professor, on the other hand, did his early research using punch cards (if you're under 20: punch cards). A simple calculation of data, not more than a few lines of code in today's world, would require a stack of punch cards that needed to be inserted in a machine and took minutes to process. The obvious comparison for me – thanks to this week's 50th anniversary of the moon landing – is that of the Apollo spacecraft computer to the super computers we carry around today in our pockets as smartphones.

Their situation reminded me of my own. They're are young tech wizards, bursting with ideas and enthusiasm. The professor, much like my supervisor, has decades of wisdom and the accumulated expertise of thousands of books and papers he read and wrote over the years.

Much like interdisciplinary teams, inter-generational teams, in my opinion, can be incredibly innovative and efficient, but only if we allow ourselves to admit that we need each other. Us young(-ish) folks are tech geeks, fluent in R, NCL, Python, and GitHub, and full of ideas for tools and gadgets, but often lack the experience and context what to innovate for. We like to make cool stuff for cool stuff's sake. Our supervisors, on the other hand, might lack some technical skills, but they know much better what problems need to be addressed.


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And that's a wrap. This was one week of work, and it was fun for me to write. If you found it insightful and/or fun to read, please feel free to drop me a note, and I might do this again in the future (maybe during a conference or workshop).