Using a new software technology combining the strengths of MEG (magneto-encephalography) and fMRI (functional magnetic resonance imaging), we are able to characterize the spatiotemporal dynamics of perceived or imagined events at the level of the whole human brain.
This project aims to build models grounded in perception that tackle classical planning problems in AI in a new realm.
Mixed-methods qualitative (interviews and coding) and computational (AI) approach to understanding relationships between social identities, cultural values, and virtual identity technologies (e.g., online profiles and avatars).
Using AI methods, we are developing an attack tree generator that automatically enumerates cyberattack vectors for industrial control systems in critical infrastructure (electric grids, water networks and transportation systems). The generator can quickly assess cyber risk for a system at scale.
Uhura is an autonomous system that collaborates with humans in planning and executing complex tasks, especially under over-subscribed and risky situations.
Graph Neural Networks (GNNs) are a powerful framework revolutionizing graph representation learning, but our understanding of their representational properties is limited. This project aims to explore the theoretical foundations of learning with graphs and relations in AI via the GNN architecture.
Our goal is to develop a socially intelligent team coacher agent that helps humans communicate, strategize, and work together efficiently.
Our research seeks to discover best practices for using avatars to enhance performance, engagement, and STEM identity development for diverse public middle and high school computer science students. As sites of our research we run workshops in which students learn computer science in fun, relevant ways, and develop self-images as computer scientists.
The impending success of AV technology will create vehicles that collect sensor data at a high rate.