Thanks for your interest! Unfortunately, we don't have such an online class, and it'll actually be in Czech.

I've already given such a talk last year, starting with some motivation and then talking about cardinality estimation (hiding many details). About 50 students attending, some interacting at the beginning. Slides in Czech (hopefully easy to translate nowadays):
docs.google.com/presentation...

Btw, Nick's profile: @nickmatsakis.bsky.social

The algorithm gives more than just an estimate for the diameter: Using the stored points, one can sqrt(2)+epsilon-approximate Furthest Neighbor queries or 1.22-approximate the Minimum Enclosing Ball. The approx. ratio for diameter is optimal but it's still open for MEB. Lots of nice open problems!

Specifically, for sqrt(2)+epsilon-approximation of diameter, the AS'10 algorithm stores O(1/epsilon^3 log(1/epsilon)) input points, and we managed to shave off one factor of 1/epsilon. Still, we can only prove a lower bound of 1/epsilon, and closing the gap is a nice open problem!

The AS'10 algorithm covers points by blurred balls, and this approach overall works. Adding a few ideas, we have circumvented the issue in AS'10, slightly simplified the algorithm and its analysis, and improved the space bounds.

We in fact simplify a nice paper by Agarwal and Sharathkumar from SODA'10 and Algorithmica '15. Yet, despite that it's published in a decent journal, there appears to be a subtle flaw in the argument, and fixing it probably requires using more space...
link.springer.com/article/10.1...

Yes! Even a full hard drive with family photos is apparently a useful computational resource.

Thanks a lot for organizing the workshop! I really enjoyed it!

@pavelvesely.bsky.social (CSI) on the mother of spss: masked superstrings that help you representing k-mer sets in a very compact way. He actually takes a lot from @brinda.eu since he squeezed 3 papers in a 12 min talk 😳