ICS Calendar

Abstract: Estimating dynamic treatment effects is essential across various disciplines, offering nuanced insights into the time-dependent causal impact of interventions. However, this estimation presents challenges…

Abstract: Respondent-driven sampling (RDS) is a network-based sampling strategy used to study hidden populations for which no sampling frame is available. In each epoch of…

Abstract: While right-censored time-to-event outcomes have been studied for decades, handling time-to-event covariates, also known as censored covariates, is now of growing interest. So far,…

Abstract: Graphs and networks are widely used to represent complex systems such as genetic regulatory networks, brain connectivity networks, etc. Learning underlying graphs from high-dimensional…

Welcome back for the 2024-25 academic year! During this seminar I will highlight department, faculty, and student achievements from our past year and welcome our…

Abstract: Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the…

Abstract: Mobile health (mHealth) interventions, such as text messages and push notifications targeting behavior change, are a promising alternative to in-person healthcare. Understanding how the…

Abstract: Many model-agnostic statistical diagnostics are based on repeatedly re-fitting a model with some observations deleted or replicated. Cross-validation, the non-parametric bootstrap, and outlier detection…

Abstract: Our society remains profoundly unequal. This talk discusses how data science and machine learning can be used to combat inequality in health care and…