I am a statistician who develops and applies statistical methodology to answer key questions in public health and medicine through thoughtful study design and analysis combined with deep collaboration with applied scientists. My work is motivated by problems across a wide array of applications, including physical activity, oncology, and substance use and related policy, and spans the entire investigative process from formulating a research question through study design and data analysis.

My goal is to develop statistical methods that empower scientists to make impactful contributions in their fields. My methodological work involves building tools to address important statistical issues in a way that is accessible and understandable to applied researchers. My work is primarily related to causal inference – the use of data to make causal conclusions through precise assumptions, strong study design, and estimation techniques – with complex repeated-measures data.

Currently, I am a postdoctoral fellow in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, working with Elizabeth Stuart, PhD and Beth McGinty, PhD on causal inference for health policy evaluation. My PhD was supervised by Daniel Almirall, PhD.

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