Abstract: Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on…
Abstract: When robots are to be deployed over long time scales, optimality should take a backseat to “survivability”, i.e., it is more important that the…
Abstract: This keynote serves as a call for awareness to understand and normalize the exceptional digital and STEAM literacy practices within Black families and youth.…
Abstract: Recent advances in computing and measurement technologies have led to an explosion in the amount of data with network structures in a variety of…
Abstract: Acoustic imaging leverages sound to form visual products with applications including biomedical ultrasound and sonar. In particular, synthetic aperture sonar (SAS) has been developed…
Abstract: Generative (gen) AI, including large language models (LLMs) and text-to-image (T2I) models, has exploded in popularity over the past couple of years. It is…
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: In this talk, we will delve into the multifaceted challenge of multi-agent deployment for critical applications such as area surveillance, wireless communication coverage, and…
Abstract: Many people face marginalization by today’s healthcare system or society at large, and those experiences often lead to healthcare inequities as well as poor…
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: Hierarchical Clustering (HC) is a widely studied problem in unsupervised learning and exploratory data analysis, usually tackled by simple agglomerative procedures like average-linkage, single-linkage…
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,…