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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.
Sensitivity analysis for causal effects under unmeasured confounding
Published in Biometrika, 2024
Bounds and diagnostics for causal effect estimates when unmeasured confounding may be present.
Recommended citation: Reyes, M., & Carter, E. (2024). "Sensitivity analysis for causal effects under unmeasured confounding." Biometrika.
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
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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.)