About Me
Hello! I am a third year PhD student in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. My research interest areas focus on causal inference with applications to infectious disease, and more broadly problems related to causal inference for social good.
I am co-advised by Nima Hejazi and Rajarshi Mukherjee.
I also enjoy photography – please feel free to check it out on my Flickr:
My research perspective is driven by several philosophical perspectives that I hold dear:
- In my work in social epidemiology and infectious disease epidemiology, I have often found that the highest quality of evidence necessarily came from observational studies where randomization is not feasible, practical, or ethical. This has led me to focus on causal inference methods for observational or semi-observational studies.
- Unfortunately, often studies rely on cross-sectional data (i.e., data collected at a single timepoint), which can obscure causal relationships. As a result, I prefer to focus on studies with time-varying exposures (often involving time-varying confounder-exposure feedback!).
- As an ethical imperative, I do not believe advanced machine learning methods should only benefit big corporations or the already-enfranchised. As such, I aspire to bring advanced machine learning methods into my causal inference work. This has led me to largely focus on the paradigm of Targeted Learning (1, 2).
My present doctoral research projects include:
- Extending the longitudinal modified treatment policies framework to efficiently estimate subgroup-specific effects with applications to how deforestation impacts malaria incidence in Madagascar.
- Leveraging positive predictive value as a measure of variable importance to study symptom log data in COVID-19 platform trials.
I am also working in a research assistantship capacity with the Mass General Brigham hospital supervised by Dr. Brian Healy, and we are focused on
- Evaluating the potentially beneficial or harmful effects of blood transfusion on patients in the ICU.
- Answering practical questions about the longitudinal course of multiple sclerosis and other neurological degenerative diseases such as Alzheimer’s and dementia.
Recently I worked (2020-2023) as a statistical data analyst with Nancy Krieger and Jarvis Chen in the Department of Social and Behavioral Sciences at the Harvard T.H. Chan School of Public Health on projects including:
- The Public Health Disparities Geocoding Project 2.0
- Two NIH R01 grant funded projects:
- DNA methylation & adversity: pathways from exposures to health inequities
- Advancing novel methods to measure and analyze multiple types of discrimination for population health research
- A COVID-19 Paper Series
Prior, I worked (2017-2020) in the Department of Global Health and Population at Harvard T.H. Chan School of Public Health with Joshua Soloman (now at Stanford) and Nicolas Menzies in the Prevention Policy Modeling Lab as a data analyst and programmer on several CDC grant funded projects.
Those projects included:
- A web-app to allow users to interact with a Bayesian simulation model of tuberculosis in the US 50 states and DC under user-configurable scenarios.
- Papers on gonorrhea, syphilis, and tuberculosis transmission in US contexts and implications for strategies for prevention.
I received my Bachelors of Science in Mathematics from Tufts University in 2017.