I had posters in two sessions:

Machine learning for social computing

and

Networks across disciplines

I got some great feedback and actually managed to understand a few talks. One interesting thing that I saw a few presentations about involved decomposing a matrix as a sum of a low rank and a sparse matrix. This can be cast as everybody’s favorite thing, and SDP! And it seems a very intuitive approach towards noisy data. I’d also like to read up about some nice posters I saw on efficient MMSBs and structure learning structure for sparse graphical models.