upcoming talks


past talks

The Low Hanging Fruit of the Twitter Following Graph
2021-08-11, Joint Statistical Meetings

In recent applied work on the Twitter media ecosystem, we have found that Twitter metadata (such as follows, likes, quotes, retweets, mentions, etc) is often more informative than the actual content of tweets themselves. The metadata, in some sense, is the right data to use for many inference tasks. In particular, we find that embedding the Twitter following graph is highly informative. However, collecting the following graph is rather challenging due to API rate limits, and storing graphs can also be challenging. We present some computational infrastructure to make access and storage of this high signal data more straightforward, and suggest that research progress would be well served by an increased focus on instrumentation. slides

Solving the model representation problem with broom
2019-01-25, rstudio::conf(2019)

The R objects used to represent model fits are notoriously inconsistent, making data analysis inconvenient and frustrating. The broom package resolves this issue by defining a consistent way to represent model fits. By summarizing essential information about fits in tidy tibbles, broom makes it easy to programmatically work with model objects. Combining broom with list-columns results in an especially powerful way to work with many model fits at once. This talk will feature several case studies demonstrating how broom resolves common problems in data analysis. video, slides

Solving the model representation problem with broom
2018-09-19, Statistics Graduate Student Seminar

Convenient data analysis with broom
2018-11-30, RStudio Webinar Series

Broom is a package that converts statistical objects into tibbles. This consistent structure makes it easier to accomplish many standard modelling tasks. In this webinar I’ll demonstrate how to use to broom to work with many models at once. We’ll see how broom makes it easier to visualize models, work with bootstrapped fits and assess model diagnostics. video, slides

Solving the model representation problem with broom
2018-09-19, Madison R User Group

slides from various informal presentations

Identifiability of homophily and contagion in social networks
2022-02-23, Madison Networks Reading Group

Triangles & networks
2021-02-17, STAT 992 Seminar on Tensors

A new way to think about citations
2020-11-17, Rohe Lab Group Meeting

The linear probability model
2019-11-19, STAT 992 Seminar Course presentation

rstudio internship progress update
2018-07-23, RStudio tidyverse team

Locally Interpretable Model-Agnostic Explanations
2018-03-29, Rice DataSci club

Your First R Package
2018-02-22, Rice DataSci club

COMP 540 Prediction Project Advice
2018-03-09, Rice University

Super Learner
2018-01-23, Allen Lab Group Journal Club

Last updated on 2022-04-27.