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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Calibrated uncertainty for deep predictive models

Published in Advances in Neural Information Processing Systems (NeurIPS), 2022

A post-hoc method for producing calibrated predictive uncertainty in deep models.

Recommended citation: Khan, S., Carter, E., & Vasquez, T. (2022). "Calibrated uncertainty for deep predictive models." NeurIPS.

Selective inference after high-dimensional model selection

Published in The Annals of Statistics, 2023

Valid post-selection inference for models chosen by high-dimensional variable selection.

Recommended citation: Carter, E., & Park, J. (2023). "Selective inference after high-dimensional model selection." The Annals of Statistics.

Scalable Bayesian inference for high-dimensional hierarchical models

Published in Journal of the American Statistical Association, 2025

A scalable variational approach to posterior inference in high-dimensional hierarchical models.

Recommended citation: Carter, E., Nguyen, A., & Olsson, R. (2025). "Scalable Bayesian inference for high-dimensional hierarchical models." Journal of the American Statistical Association.

talks

teaching

STAT 210: Probability Theory

Undergraduate course, Cascadia University, Department of Statistics, 2024

Foundations of probability: random variables, common distributions, expectation, and the classical limit theorems. (Example entry — replace with your own course.)

STAT 305: Applied Bayesian Statistics

Advanced undergraduate / graduate course, Cascadia University, Department of Statistics, 2025

Bayesian modeling and computation — priors, hierarchical models, MCMC, and applied case studies. (Example entry — replace with your own course.)

STAT 520: Statistical Machine Learning

Graduate course, Cascadia University, Department of Statistics, 2025

Theory and methods for prediction, with an emphasis on uncertainty quantification, calibration, and the statistical foundations of modern models. (Example entry — replace with your own course.)