According to FiveThirtyEight, Democrats need to win the popular vote by a 5.6% margin in order to take 50% of the seats in the House of Representatives this November. This margin is partly due to Republicans having an advantage since they are in control of the House already, partly because Republicans have a natural advantage due to geographic reasons, and partly due to gerrymandering. How much of it is accidental and how much is due gerrymandering? What techniques do we have to identify and mitigate gerrymandering? These questions were part of the focus of last week’s Quantitative Redistricting workshop in Duke University, organized by SAMSI. I had the fortune to attend this workshop and I am writing summary of some of the interesting ideas presented.
In theory, theory and practice are the same, but in practice, they’re not. This phrase is probably a cliché but I think it captures part of the spirit of Ben Recht’s “quixotic quest for super-linear algorithms” talk on Tuesday at Simons.
This week I’ve been attending this conference on Topological Data Analysis. On Thursday both Lek-Heng Lim and Sayan Mukherjee talked about HodgeRank (their slides are available on Sayan’s website). I think it’s a really neat application of cohomology to a simple problem that illustrates what information you can get from cohomology groups. So I’m writing about it in my first post.
My friend and collaborator Dustin Mixon
has been trying to convince me to start a blog for a while. When I mentioned it
to Chris Carson he advised me to do it on Github Pages using Jekyll (thanks Chris for the help!). Let’s see how this goes.
This post is about my latests preprint with Dustin Mixon about signal denosing with generative networks.