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,…
Abstract: There has been a spike in concern about existential risk from artificial general intelligence, or AGI. This fear, commonly associated with terms such as…
Abstract: Methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion to solve planning problems. However,…
Abstract: Dr. Kaiser will discuss her past work applying AI-based techniques to software engineering problems and applying software engineering techniques to finding bugs in AI…
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…