Randomized experiments are the gold standard for causal inference, and justify simple comparisons across treatment groups. Regression adjustment provides a convenient way to incorporate covariate…
In practical reinforcement learning (RL), a representation of the full state which makes the system Markovian and therefore amenable to most existing RL algorithms is…
Welcome back for the 2023-24 academic year! During this seminar I will highlight department, faculty, and student achievements from our past year and welcome our…
We investigate the robustness of the model-X knockoffs framework with respect to the misspecified or estimated feature distribution. We achieve such a goal by theoretically…
Rapid developments in streaming data technologies have enabled real-time monitoring of human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy),…
Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying…
In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or…
Multilayer networks continue to gain significant attention in many areas of study, particularly, due to their high utility in modeling interdependent systems such as critical…
Estimating large covariance/precision matrices are fundamental problems in modern multivariate statistics. Virtually all of the existing methods in this literature assume independent samples. In the…
When conducting analysis of electronic health records (EHR), oftentimes the data utilized is patient level data which readily allows for statistical analyses that properly adjust…
Abstract: Parametric models for networks with heterogeneity and/or complex dependence have seen considerable progress over the past two decades, opening the door to further modeling…
Abstract: We propose a model to flexibly estimate joint tail properties by exploiting the convergence of an appropriately scaled point cloud onto a compact limit…