In a nutshell: I had an incredible time and would highly recommend it to anyone who is analyzing scientific data and doesn’t have extensive training in computer science or software engineering practices.
I wasn’t entirely sure what to expect (in terms of content, presumed background knowledge, etc.), which made me feel slightly nervous going into it. At the same time, I was super excited to spend a whole week delving deeply into the best practices of scientific programming. Importantly, my energy levels and social battery were pretty high going into it. I thought it would be a great opportunity to chat with the other participants about their career choices and PhD / Postdoctoral experiences.
Each day consisted of interactive lectures, during which we covered topics such as version control using git, the fundamentals of computer architecture and scientific programming patterns. The lectures always included programming exercises and often also live coding demonstrations. For the programming exercises, we worked together in pairs, switching partners for each new topic. This was a completely new experience for me. Not only did it teach me a lot about how other people approach coding problems, it also made me more aware of my own weaknesses. In the past, programming has always been an individual experience for me. But this carries the risk of writing “bad” code and ultimately leads to the development of bad habits (since there is no one to correct you). I have finally come to realize that programming, at its core, is all about problem solving. This means it is almost always a good idea to take a minute to sketch out a game plan, either silently in your head, in written format, or by discussing it with a colleague. And if no one is available, you can always present it to a little rubber duck on your desk (always talk to the duck :D).
You know it has been a good learning experience when the time just flies by. Even though the days were long and we covered a lot of content, it was always fun and very collaborative. Everyone (from the faculty + the participants) was super approachable and happy to share their academic experiences with me.
My main takeaway: I will be more mindful of my own coding hygiene and habits (e.g. moving away from Jupyter notebooks and learning how to use debugging tools properly). I am also incredibly grateful that I had the opportunity to attend ASPP at such an early stage in my career.
]]>Full disclosure: I have not. With my background in Psychology and Cognitive Neuroscience, I have always been a “user” of mathematics. I received training in Statistics and learned which formula to apply to solve a specific problem. Throughout my life, I always felt a bit intimidated by maths. However, this changed in the last years of high school (thanks to my great maths teacher).
And here I am, listening to the mathematician Emily Riehl as she describes alternative ways of proof writing. She talks about category theory as a mathematical foundation system and using computer-based proof assistants to keep track of your steps in the proof. The whole process seems extremely meticulous. I understand maybe 40% of her talk, and even less as she walks us through a particular proof that relies on something called the Yoneda Lemma. To me sounds this sounds more like a character straight out of a science fiction story rather than anything related to math.
Side note: It actually is the fundamental theorem of category theory, named after the Japanese mathematician Nobuo Yoneda.
I do find it remarkable how fast and effortlessly she speaks about various mathematical objects and their relations, without ever stumbling over her words. The examples that she gives are drawn from her own research in category theory. In a nutshell, category theory provides a high-level framework to group mathematical objects with respect to their relations (for an in-depth / mathematically more rigorous explanation see: https://emilyriehl.github.io/files/context.pdf)
It feels like a new language to me. Like in the beginning when I started learning Italian and I couldn’t tell just from listening when one word ended and the next one began. Learning maths feels somehow similar. It just takes time, exposure, and patience to start making sense of it.
Here is some work by Emily Riehl (targeted at a non-mathematician audience):