Degrees
Sc.D., Harvard School of Public Health (1999)
A.B., Harvard College (1987)
Professor of Statistics
Sc.D., Harvard School of Public Health (1999)
A.B., Harvard College (1987)
I have taught a variety of courses in statistics and related fields, including probability, mathematical statistics, regression and design of experiments. I'm passionate about improving quantitative literacy for students with a variety of backgrounds (for introductory statistics) as well as engagement and mastery of higher-level concepts and capacities to undertake research (for the upper level courses). I'm honored to have been the recipient of a number of teaching awards.
The continued growth of interdisciplinary scientific research as well as the advent of big data has been a hallmark of the past few decades. It is increasingly important to be able to connect disciplines in order to further scientific knowledge. As an applied biostatistician, my work is based squarely within the mathematical sciences, but spans other fields in order to ensure that research is conducted on a sound footing. The real-world research problems that these investigators face often require the use of novel solutions and approaches, since existing methodology is sometimes inadequate. Bridging the gap between theory and practice in interdisciplinary settings is often a challenge, and has been a particular focus of my work.
My statistical methodological research continues to focus on the development of approaches to account for multivariate response models, longitudinal studies and missing data. I recently completed a fellowship through the ASA/NSF/Bureau of Labor Statistics program, where I led a project to improve imputation methods for the Occupational Employment Survey.
In addition to developing new methods, there is a pressing need for statisticians to help disseminate and promulgate the use of modern approaches, as well as to to help ensure that scientific investigations are conducted on a solid statistical footing. Many of these projects have involved undergraduate students. I welcome these opportunities to help introduce students to research and consider this a key part of my teaching and scholarship.
R is a powerful open source environment for statistics. RStudio is an integrated development environment that facilitates the use of R by students and instructors. A server is available for members of the Amherst community at r.amherst.edu The following resources are intended to help those getting started with these systems.
Five College Guide to R and RStudio
Minimal (1 page) guide to R for intro stats
Getting started with RStudio: logging in to the server
Getting started with RStudio: first steps in R
Getting started with RStudio: second steps in R
Getting started with RStudio: first steps with R Markdown
Getting started with RStudio: second steps with R Markdown
Getting started with RStudio: sample homework in markdown
Getting started with RStudio: dealing with files
Getting started with RStudio: all about packages
Getting started with RStudio: other resources
Precursors to Data Science in R
Using R for Data Management, Statistical Analysis, and Graphics
Just as a reminder, in case of questions, there are drop-in statistics hours each week-night coordinated with the Moss Quantitative Center.