Degrees
Sc.D., Harvard School of Public Health (1999)
A.B., Harvard College (1987)
Beitzel Professor in Technology and Society (Statistics and Data Science)
Sc.D., Harvard School of Public Health (1999)
A.B., Harvard College (1987)
I have taught a variety of courses in statistics, data science, and related fields, including probability, mathematical statistics, regression and design of experiments. I'm passionate about improving quantitative and data 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 and to have the privilege of working with our amazing students, wonderful staff, and highly effective faculty colleagues.
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 and data scientist, my work is based squarely within the mathematical, statistical, and computational sciences, but spans other fields in order to ensure that research is conducted on a sound footing. The real-world data 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 statistics and data science settings is often a challenge, and has been a particular focus of my work.
My statistical methodological research focuses on the development of approaches to account for multivariate response models, longitudinal studies and missing data. I 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. I have also been participating in several data science education initiatives through the National Academies.
In addition to developing new methods, there is a pressing need for statisticians and data scientists 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.