|| Biostat prof @ UW | interests in pedogogy, critical theory, chess (pretty bad at it) | he/him ||
Joined May 2019
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What's everyone's favorite package/approach to engage with tensorflow/keras in R? (trying to teach a short course, and don't want to require ppl to know python; unfortunately reticulate->miniconda seems to have a bug in windows =[)
Just came across a nugget of wisdom from a mathematician that I thought I would share: "A drunk man will find his way home, but a drunk bird may get lost forever" - Shizuo Kakutani
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She may look cute, but her favorite activity is eye gouging. no joke. (to be fair, it was a rough week for everyone...)
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Wanted to share a lovely paper primarily by @StatTZhang that was just accepted at Annals of Statistics on efficient non-parametric regression using stochastic optimization! arxiv.org/abs/2104.00846 Less technical summary to follow... (1/n)
Why aren't there any good golden snitch costumes for infants? Also, how dare you suggest I plan to dress my baby up as the golden snitch while I watch the secrets of dumbledore... (when it is streamable)
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This is quite useful (especially for people looking to get into ML); my summary of the thread is that the ability to engage in simple, fit-for-purpose modeling, and then validate+communicate your models trumps all else.
Universities do a terrible job teaching machine learning.
Not only do they give you critically out-of-date information, but they focus most of their time on the least important aspects.
Here 5 things everyone in industry WISHES your professor taught you:
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awesome work by Jean on how to get the most out of neural networks (with less work) for high dimensional biomedical problems!
Our paper came out on Pi day! We describe how to train and tune neural networks for high-dimensional data using only TWO hyper-parameters (doi.org/10.1002/sam.11579). We think it's easier, so we've called it EASIER-net. @StatsSimon
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Really excellent tutorial! (By far the clearest treatment of semiparametric inference/efficiency I have read!)
I wrote a review & tutorial illustrating how I think about semiparametric / doubly robust / TMLE / DML methods:
arxiv.org/pdf/2203.06469.pdf
It builds on workshops I've given in the past using these slides:
xitter.gq/edwardhkennedy/status/…
(next one's at ACIC in May: ctml.berkeley.edu/american-c…)
Been making some intro asymptotic stats theory videos (for my course). Perhaps others might find them useful. Thumbnails are pictures of the baby! More videos to come soon!
youtube.com/playlist?list=PL…
Great article (and quite accessible!)
Prof. Bradley Efron has written a lovely article about statistics in the age of deep learning.
I hope this becomes essential reading for anyone engaged in machine learning.
statweb.stanford.edu/~ckirby…
really good general presentation advice!
I've put together presentation advice for econ theory talks.
It's coming that time of year when many young theorists rapidly learn to present papers, so I thought this might be useful.
drive.google.com/file/d/1SHi…
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This is why we need polyak averaging for dogs.
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A very nice analysis of what is going on with the Pfizer efficacy data against delta variant in Israel (and why it actually tells a consistent story that the vaccine is still highly effective). Great example of Simpson's paradox/confounding!
In this blog post, I go through the recent Israeli data in detail to explore what it tells us about efficacy of the Pfizer vaccine vs. severe disease with the Delta variant.
#deltavariant #israel #pfizervaccine #vaccines #covid19pandemic
covid-datascience.com/post/i…
Awesome and fascinating talk by @uhlily on "the construction of social causation" at UW biostat today! I encourage everyone to read all of her stuff! (and consider how stat/biostat/epi analyses create and reaffirm social norms!)
I'm planning a one-off guided data analysis in R for a group of high school students. Any suggestions on how to streamline the setup process? I was thinking maybe Rstudio cloud? (and just have everyone run stuff through their browser)
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A pretty brilliant piece by @uhlily challenging the way statistics and causal inference is used to engage with racism
Thank you to @yc_yc_yc_yc for sharing this great read by @uhlily phenomenalworld.org/analysis…. I encourage my @UWBiostat colleagues to give it a read. #causaltwitter
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It's going to be pretty fun!!! I may even tell my famed "biostatistician joke"
Check out the amazing list of contributors to the upcoming
(virtual) Sixth Seattle Symposium in Biostatistics! Four days of fascinating talks, including session on data science in COVID-19. See full program at bit.ly/368zm08. Register by Nov 17 to join in!
