talks

This page contains material from my past presentations. Many of these presentations are very similar to each other, but I have nonetheless included all the variations on my slides because I find the evolution of presentations (and multiple views on the same problem) to be interesting and informative.

For those of you who prefer skimming slides to reading papers, I’ve highlighted a few presentations below that are good places to start.

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Estimating peer influence: limitations of linear-in-means models
2024-11-22 @ 5 pm, Wisconsin ASA Chapter Meeting
poster

Estimating peer influence: limitations of linear-in-means models
2024-11-12 @ 2:35 pm, American Family Funding Initiative Networking Meeting
poster

Summary of my work on network mediation

Estimating network-mediated causal effects via spectral embeddings
2024-06-17 @ 11:45 am, SINM Satellite, NetSci 2024
slides

Summary of my work on colinearity in the linear-in-means model

Asymptotic identification of peer effects in linear models (thesis defense)
2024-04-04 @ 12:30 pm, SMI 133, Medical Sciences Center
slides

Peer effects are parametrically indistinguishable from baseline behaviors in the asymptotic limit
2023-11-27 @ 4 pm, Computer Science 1325, Statistics Grad Student Seminar

Latent contagion in low-rank networks
2023-10-11 @ 2 pm, SMI 133, Levin Lab Meeting
slides (private for the time being)

Peer diffusion over uncertain networks
2023-09-18 @ 12:30 pm, WID 1145, IFDS Ideas Seminar
slides (private for the time being)

Estimating network-mediated causal effects via spectral embeddings
2023-08-09 @ 10:30 am, Recent Developments in Causal Inference, JSM 2023
slides

Estimating network-mediated causal effects via spectral embeddings
2023-05-24 @ 5:30 pm, Poster Session 1, ACIC 2023
poster

Estimating network-mediated causal effects via spectral embeddings
2023-04-24 @ 12:30 pm, Orchard View @ the WID, IFDS Ideas Seminar
slides

Estimating network-mediated causal effects via spectral embeddings
2022-10-14 @ 4:15 pm in MSC 1210, Statistics Graduate Student Association Seminar
slides

Summary of my network mediation work for a spectral networks audience

Estimating indirect effects induced by homophily via spectral network regression
2022-07-07, Tianxi Li and Can Le Joint Lab Meeting
slides

distributions3: From basic probability to probabilistic regression
2022-06-23, UseR 2022
Achim Zeileis, Moritz Lang and Alex Hayes

The distributions3 package provides a beginner-friendly and lightweight interface to probability distributions. It allows to create distribution objects in the S3 paradigm that are essentially data frames of parameters, for which standard methods are available: e.g., evaluation of the probability density, cumulative distribution, and quantile functions, as well as random samples. It has been designed such that it can be employed in introductory statistics and probability courses. By not only providing objects for a single distribution but also for vectors of distributions, users can transition seamlessly to a representation of probabilistic forecasts from regression models such as GLM (generalized linear models), GAMLSS (generalized additive models for location, scale, and shape), etc. We show how the package can be used both in teaching and in applied statistical modeling, for interpreting fitted models, visualizing their goodness of fit (e.g., via the topmodels package), and assessing their performance (e.g., via the scoringRules package). video, slides

Summary of computational work on Twitter data and an analysis of #rstats Twitter

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

An early, informal presentation about my work on co-factor analysis for citations networks

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

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

Solving the model representation problem with broom
2018-11-30, Statistics Graduate Student Seminar
slides

Convenient data analysis with broom
2018-11-14, RStudio Webinar Series
video, slides

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