PeopleofSuperintelligence

Researcher

Alex Hayes

Alex Hayes is a statistics researcher whose work on causal inference and networks includes a 2025 study in the Journal of Machine Learning Research. He studied statistics at Rice, interned at RStudio, worked on the `broom` R package, and coauthored a 2019 paper on the tidyverse. His later research develops methods for citation networks and peer effects, including estimation from noisy networks. He is an assistant professor in Oregon State’s Department of Statistics and Data Science, a role he began in September 2026.

Works
21
Citations
21,790
h-index
4

Publication numbers from OpenAlex, as of 15 October 2026.

Timeline

  1. 2018–2019

    Rice statistics and Tidyverse software

    In 2018, he studies statistics at Rice and interns at RStudio. The internship pairs him with Dave Robinson on pull requests and reorganization of the `broom` package. In 2019, he coauthors “Welcome to the Tidyverse” for the Journal of Open Source Software.

  2. 2024

    Co-factor analysis of citation networks

    In 2024, he and Karl Rohe propose two citation-network embeddings: one captures papers’ outgoing citations, and the other captures citations they receive.

  3. October 2024

    Limits of peer-effect estimation

    In October 2024, Hayes and Keith Levin show that the linear-in-means model can identify peer effects that remain hard to estimate because of near-perfect collinearity.

  4. 2025

    A Wisconsin statistics PhD

    In 2025, he researches at the intersection of statistics and economics, completes a PhD in Statistics at the University of Wisconsin–Madison, and is co-advised by Keith Levin and Karl Rohe.

  5. 2025

    Causal network regression and an award

    He wins the American Statistical Association’s Best Student Paper award in Business and Economics in 2025. With Mark M. Fredrickson and Keith Levin, he publishes a Journal of Machine Learning Research paper that uses principal-components network regression to separate direct from network-mediated causal effects.

  6. 2025–2026

    A postdoctoral role at Stanford

    At his 2025 graduation, he is set to take a postdoctoral position in Stanford Economics. Before he joins Oregon State in September 2026, he works as a postdoctoral scholar in Stanford’s Economics Department.

  7. May 2026

    Smoothing noisy networks

    In May 2026, he and Keith Levin propose a low-rank smoothing method to estimate peer effects from noisy networks.

  8. Since September 2026

    Assistant professor at Oregon State

    He joins Oregon State University as an assistant professor in Statistics and Data Science in September 2026.

Sources and credits

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  1. Summer Interns posit.co. Checked 15 October 2026.
  2. about – alex hayes alexpghayes.com. Checked 15 October 2026.
  3. Journal of Open Source Software: Welcome to the Tidyverse joss.theoj.org. Checked 15 October 2026.
  4. [2408.14604] Co-factor analysis of citation networks arxiv.org. Checked 15 October 2026.
  5. [2410.10772] Minimax rates for the linear-in-means model reveal an identifiability-estimability gap arxiv.org. Checked 15 October 2026.
  6. Department of Statistics celebrates spring 2025 graduates stat.wisc.edu. Checked 15 October 2026.
  7. Estimating Network-Mediated Causal Effects via Principal Components Network Regression jmlr.org. Checked 15 October 2026.
  8. [2605.03204] Estimating peer effects in noisy, low-rank networks via network smoothing arxiv.org. Checked 15 October 2026.
  9. Alex Hayes | Department of Statistics stat.oregonstate.edu. Checked 15 October 2026.