Topology and Netflix problem

The Netflix problem (from machine learning) asks the following. Given a ratings matrix in which each entry $(i,j)$ represents the rating of movie $j$ by customer $i$, if customer $i$ has watched movie $j$, and is otherwise missing, we would like to predict the remaining entries in order to make good recommendations to customers on what to watch next. The remaining entries are predicted so as to minimize the rank of the completed matrix.
We study a more general problem, in which instead of knowing specific matrix elements, we know linear relations on such elements. We shall describe applications of these results to embeddings of graphs in surfaces (including embedding modulo 2), and of $k$-dimensional complexes to $2k$-dimensional manifolds. See arXiv:1903.08637, arXiv:1904.02404, arXiv:2012.12070, arXiv:2106.14010, arXiv:2112.06636, arXiv:2208.04188.

Zoom (new link!): https://zoom.us/j/97302991744
Access code: the Euler characteristic of the wedge of two circles
(the password is not the specified phrase but the number that it determines)