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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Blog Post number 1
Published:
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portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
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publications
Improving the Estimation of Site-Specific Effects and their Distribution in Multisite Trials
Published in , 2021
Link to paper on Arxiv
Athlete Rating in Multi-Competitor Games with Scored Outcomes via Monotone Transformations
Published in , 2022
Link to paper on Arxiv
Caliper Synthetic Matching: Radius Matching with Adaptive Calipers and Local Synthetic Controls
Published in , 2022
Poster presented at American Causal Inference Conference 2022
On power analyses for individual site impacts in multisite trials
Published in , 2022
Slides presented at Society for Research on Education Effectiveness Conference 2022
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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teaching
STAT 149: Generalized Linear Models
Undergraduate course, Harvard University, 2020
Course abstract: Sequel to Statistics 139, emphasizing common methods for analyzing continuous non-normal and categorical data. Topics include logistic regression, log-linear models, multinomial logit models, proportional odds models for ordinal data, Gamma and inverse-Gaussian models, over-dispersion, analysis of deviance, model selection and criticism, model diagnostics, and an introduction to non-parametric regression methods.
STAT 211: Statistical Inference
Graduate course, Harvard University, 2020
Received Certificate of Distinction in Teaching (Fall 2020)
EDU S043: Multilevel and Longitudinal Models
Graduate course, Harvard University, 2021
Course abstract: Data often have structure that needs to be modeled explicitly. For example, when investigating students’ outcomes we need to account for the fact that students are nested inside classes that are in turn nested inside schools. If we are watching students develop over time, we need to account for the dependence of measurements across time. If we do not, our inferences will tend to be overly optimistic and wrong. The course provides an overall framework, the multilevel and generalized multilevel (hierarchical) model, for thinking about and analyzing these forms of data. We will focus on specific versions of these tools for the most common forms of longitudinal and clustered data.