Department of Biostatistics & Bioinformatics

“My methodological research focuses on the theory and applications of machine learning in causal inference. Specific areas of interest include vaccine statistics, competing risks, complex longitudinal data, and theory of robust nonparametric statistical inference. My collaborative research includes work on preventive vaccines, tuberculosis, and HIV prevention.”

“In outbreaks big and small, situational awareness is vital. Despite the wealth of data that surround us, and despite advances in analytics during the pandemic, there is still much work that remains. The Center for Infectious Disease Modeling & Analytics and Training Hub promotes the timely and reliable use of data for decision-making, while also building the public health workforce who can deploy these tools. Our goal is to undertake the ‘peace time’ work needed to ensure readiness for a future public health emergency.”

"This is really the core objective of causal inference—trying to understand the consequences of interventions or think about these counterfactual scenarios—and trying to quantify the relation between the outcomes and the treatments that we're interested in. That's very different from what AI predictive models do, which focus on finding patterns. Both are useful, but we have to be careful about how we are going to use them and in what settings."