About me

I’m currently a PhD student in the Program in Population Health Sciences and the Department of Epidemiology at the Harvard T.H. Chan School of Public Health. I’m also completing a Master of Science degree in the Department of Biostatistics. My research interests are in epidemiologic and biostatistical methods as well as reproductive and child health. I developed these interests at U.C. Berkeley, where I earned my M.S. in epidemiology, and while teaching third grade at Little Wound School in Kyle, SD, where I lived for three years. Prior to that I studied comparative literature (focusing on literary translation from French and Portuguese) and community health at Brown University.

My passion for teaching has continued beyond my experience in elementary education. I enjoy introducing students to epidemiology and biostatistics as well as delving into more advanced topics in causal inference. I love to create teaching materials, some of which can be found below.

I also love hiking and camping, reading novels, and programming in R.


We show that it is possible to bound the total composite bias due to unmeasured confounding, selection, and differential misclassification, and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases.

Understanding the way in which a study sample relates to the target population is critical for avoiding and addressing bias. Communication about selection bias is aided by the use of causal graphs.

We extend a sensitivity analysis approach for selection bias to account for poor control selection in case-control studies.

We introduce the concept of mediation and provide examples that solidify understanding of mediation for valid discovery and interpretation in the field of reproductive medicine.

We provide bounds for selection bias under a variety of situations.

Online tools and projects

Introduction to R

An short online course with lecture videos and exercises to teach R basics.

EValue R package

An R package for calculating E-values for unmeasured confounding, selection bias, and misclassification

Simple sensitivity analysis for selection bias

An online tool to help assess the possible extent of selection bias.

PHS 2000 summer preparation

A learnr tutorial to introduce first-year PhD students in Population Health Sciences to probability, statistics, and R.

R office hours

A collaborative effort to develop weekly teaching materials for R help.


Courses for which I’ve been a instructor/teaching fellow

Theories and Methods for Causality I

Structural causal models, graphical models, algorithms for identification

Introduction to R

Developed and taught short course to introduce R to graduate students. Topics included creating figures and tables, cleaning data, writing functions.

Introduction to Epidemiology

Co-instructor of epidemiology course for participants in the Harvard Summer Program in Biostatistics & Computational Biology.

Advanced Epidemiologic Methods

Causal inference for time-varying exposures: g-formula, marginal structural models, g-estimation of structural nested models; static and dynamic treatment regimes

Quantitative Research Methods in Population Health Sciences

Regression models, sampling, longitudinal and multilevel analysis, time-varying confounding, mediation and interaction, econometric methods, missing data

Introduction to Epidemiology and Human Disease

Basic epidemiologic methods, overview of epidemiology of diseases/conditions of public health importance

Epidemiologic Methods

Study design, sampling, data collection, analysis, inference


You can contact me here or send me an email.