Posts

  • Quantitative gerrymandering

    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.

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  • Generative models are the new sparsity?

    denoising using generative models This post is about my latests preprint with Dustin Mixon about signal denosing with generative networks.

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  • Pywren, the cloud, and certificates of optimality

    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.

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  • Two commencements

    This post is about the end of my PhD and the beginning of a new chapter.

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  • An interview in Short, Fat Matrices

    Recently, I was interviewed by Dustin Mixon for Short, Fat Matrices. The interview is about our recent paper, A polynomial-time relaxation of the Gromov-Hausdorff distance, with Afonso Bandeira, Andrew Blumberg and Rachel Ward. Thanks Dustin for your thoughtful questions!
  • Gromov-Hausdorff and matching