Heather Eads is an Assistant Professor of Instruction in the Department of Statistics at Carnegie Mellon University. Her research interests include statistical methods for causal inference, missing data, and high-dimensional data.
- Causal inference: Eads' research focuses on developing statistical methods for causal inference, which is the process of drawing conclusions about cause-and-effect relationships from data.
- Missing data: Eads also works on statistical methods for handling missing data, which is a common problem in many real-world datasets.
- High-dimensional data: Eads' research interests include statistical methods for analyzing high-dimensional data, which is data that has a large number of features or variables.
- Teaching: Eads is a passionate educator and mentor. She has received several teaching awards, including the Carnegie Mellon University Teaching Excellence Award in 2019.
- Mentoring: Eads also serves as the faculty mentor for the Women in Statistics affinity group at Carnegie Mellon University.
- Leadership: Eads is a leader in the field of statistics. She is a member of the American Statistical Association and the Institute of Mathematical Statistics.
Eads' research has been published in leading statistics journals, including the Journal of the American Statistical Association, the Journal of the Royal Statistical Society, and the Annals of Statistics. She is also the co-author of the book "Statistical Methods for Causal Inference" (2020).
| Heather Eads | Assistant Professor of Instruction | Department of Statistics, Carnegie Mellon University |
