publicMyFaceI direct the Pew Research Center’s Data Labs. My research interests include elite communication and politics, constituent communication, social media, political advertising and campaigns, race, machine learning, experiments, networks, text as data, etc.

My work has appeared in Science, the Princeton University Press, the American Political Science Review, Public Opinion Quarterly, Scandinavian Political StudiesRadiology, Pediatric Radiology, and has been covered in the New York Times, the Washington Post, the BBC, CBS, Huffington Post, Ars Technica, Wired UK, The VergeMischiefs of FactionMashable and JunkCharts among other outlets.

Here’s my info: Google Scholar, R Bloggers, Twitter.
email: [firstname.lastname] AT gmail DOT com

 

4 Responses to

  1. John Page says:

    I enjoyed working through your affiliation data webpage at
    http://www.web.stanford.edu/~messing/Affiliation%20Data.html

    Don’t you also have to redo “V(magallg)$degree = degree(magallg)” for magallggt1 in order to get the final graph? I didn’t see that line included.

    Thanks.

  2. Richard Rice says:

    Hi Solomon,
    I have 2 questions:
    1. Will Data Analysis discuss Bayes theorem?
    2. This second question is a little longer. First some background. I
    collect weed species data in the field for the Washington state dept
    of natural resources(DNR). There are several Android apps for
    identifying weeds by looking at photos but none that I can find by
    comparing a photo that I take with my Android phone with a
    downloadable database of known weed species. What I’d like to do is
    write an app that downloads DNR’s weed photos of which there are about
    1200 to my phone, find a weed in the field, take a photo and compare
    it with DNR’s photos to see if there is a match.
    Does this sound feasible? I want to keep it simple, not try to
    identify every weed in existence, just here in Washington.

    Best Regards,

    Richard

    PS: I tried going through the Discussion Forum but didn’t get a response.

    • Solomon says:

      Hi Richard, I’m not sure what you’re referring to the first part of your question, but for your second, take a look at caffe (http://caffe.berkeleyvision.org/) which provides some libraries to train image classifiers. What you’re proposing sounds like it will require a large set of images for training so I’d recommend thinking about incorporating crowd-sourcing to come up with a large and reliable training set.

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