AEESP 2019 is now officially in full swing, and the information overload is delightful.
This year’s conference has the poster sessions at the start of the day, and it’s been an interesting change. As a presenter, I can say it’s definitely nice to start off refreshed and relaxed. Like all poster sessions, it was periods of standing around punctuated by abrupt mini-talks. But these mini-talks were more fun than usual; a little bit of that is because it felt like the browsers were more relaxed,too. There’s also another reason - there’s more common ground here than at a more general conference, and that means less explaining ‘what a wastewater treatment plant is’ and more geeking out together over the possible interpretations of the data. If I have one complaint so far about the conference, it’s that I’m a little sad that I didn’t really get a chance to browse the posters from the same presentation day. However, my super secret sources (my PI and lab mates) have sent me a list of poster #’s and you’ll be hearing from me one way or another.
As I mentioned earlier, I’ve had a lot of trouble choosing between talks. This means lots and lots of room hopping between tracks - I am pretty sure I’ll be dreaming about some of the room names tonight. Many props to the moderators and speakers, it was very rare for a talk to run long, letting me dart about MU with abandon.
I’m looking at my notes for the day, and the keyword is variety. This is semi-intentional, I try to sometimes pick a session well outside my comfort zone. I also avoid talks by my NCSU colleagues; as interesting and amazing as their work is, I can pick their brains anytime. The talks ranged from algae (both friend and foe), to self-healing living membranes, to pedagogy, including a scenario where Brad Allenby cracked our heads open and scrambled our brains (metaphorically).
Despite this diversity, the talks had a lot in common. Everybody clearly cares about their research as a means towards making the world better, rather than as a means towards graduation, positions, tenure, or grants. There’s also much more embracing of data science, and not just Data Science As A Topic, but data science as an incidental tool (e.g. casual mentions of large sensor networks, or using ‘some machine learning to help us sort this out’). Pretty cool, given some of the points raised during the second plenary.
I’m still digesting a lot of the deeper implications of some talks (and probably will be for a while), but I’m definitely jazzed and inspired. I take notes electronically on a tablet and whenever a talk generates a new idea for me to follow up, I try to write in the margin in a different color. Let’s just say I have a whole page of marginalia now. And we’re only halfway through the talks. Keep drinkin’ from that firehose y’all.