Hacking academia for fun and profit
Thoughts on succeeding in academia despite doing good software
Keynote I gave at the Scipyconf Argentina 2014 conference
The advancement of science is a noble cause, and academia a fierce battlefield for tenure. Software is seen as a mere technicality, not worth a line on an academic CV. I claim that, on the opposite software, is the new medium of scientific method. I claim that succeeding in academia can be achieved not despite writing good software but via such an accomplishment. The key is to choose the right battles and to win them.
What is the emerging role of software in the scientific workflow? Which are the software challenges that can have impact? How to balance software quality assurance and the quick turn-around random-walk of research? What does "good design" mean for research software? What Python patterns can boost productivity and reuse in exploratory scientific computing?
I will try to answer these questions, based on my personal experience of growing up to become an academic Pythonista.