It's been a while since my last activity / contest participation on here! I've largely moved on from competitive programming to machine learning, but still tried to keep up with the vibrant community of sport programmers whenever possible.
Recently I've decided to orient my focus in ways in which we can combine the representational power of neural networks (great for handling big, noisy, "real-world" data) with the incredible robustness of algorithms (provably correct, trivially generalisable across problem instances, compositionality w.r.t. subroutines and functions). One way forward is teaching neural nets the execution steps of algorithms -- i.e. replicating the intermediate outputs on classical competitive programming algorithms and tasks.
I produced the following slide deck (gave as keynote at WWW'20 Workshop on Deep Learning for Graphs) which outlines the recent explosion of work in the area (including my own). There is credit to Codedorces in one of the slides -- as one of the places that really got me into algorithmic programming / computer science in general -- including a photo of the ACM-ICPC team from Cambridge I coached (dd__, MeinKraft, zDule98) that won NWERC'17. :)
Hope you'd find it interesting!