AI-driven analysis in Neuroscience/Cancer/Cardiology, combining clinical insight with complex data to advance medical research and care. This research area brings together key projects in medical data science, addressing both clinical and public health challenges through AI-driven methods and interdisciplinary collaboration: Cunningham PB, Gilmore J, Naar S, Preston SD, Eubanks CF, Hubig NC, McClendon J, Ghosh […]
This area explores how to make AI more transparent and trustworthy—developing methods that explain, test, and strengthen AI systems in complex or adversarial environments. Towards Virtual Patient-Based Training This project develops interactive virtual patients—human-like, conversational agents with both verbal and non-verbal behaviors that resemble face-to-face interactions. Built with Unity 3D, the system offers an active, […]
From hurricane prediction to data platform generation, this research brings together timeseries analysis and data fusion—turning scattered signals into actionable insight when disaster strikes. This research area focuses on disaster prediction and response through AI-driven approaches. From flood detection and streamflow modeling to timeseries analysis and satellite image interpretation, the work combines deep learning with […]
Focuses on the emotional behavior of avatars to support self-reflection, blending insights from HCI, virtual reality, and reinforcement learning. We have designed and developed interactive virtual patients—human-like characters that are specifically conversational (both verbal and non-verbal) in their behaviors and present the similar properties as humans in face-to-face conversations. Developed using Unity 3D, the virtual […]
Nina Hubig
