At the Institute for Experiential AI, our goal is to combine the best theories with the best practices to understand when AI works and when it doesn't. Our principled research approach extends beyond the typical "black box + Big Data" formula to include human input and engagement at all project phases from conception to completion, review, and revision.
Currently, our applied research areas include healthcare, life sciences, cybersecurity, and complex networks, among others. Our formal research work includes Responsible AI and the next generation of search technology that we call Information Retrieval 3.0.
Complex networks can model so many human-based processes, applications of AI in practice are innumerable. The Institute for Experiential AI works with network scientists to develop predictive and descriptive models to help understand the phenomena associated with these types of networks.
At the Institute for Experiential AI, we have several core faculty members with AI health expertise who cover AI for healthcare from multiple scales and various heterogeneous data sources.
From climate science and water sustainability to urban resilience and disaster preparedness, Artificial Intelligence has shown significant potential to inform and enable transformative action in the areas of climate, sustainability, and the environment.
We want to collaboratively learn global models that preserve the data privacy of individual contributors and achieve better detection capabilities than local models trained by a single enterprise.
We are building a multidisciplinary team of experts and end-users aligned to bring impactful solutions to the market to establish a new standard across the academic and industry sectors.
IEAI faculty are developing real-time, robust adaptive learning algorithms to channel conditions while also meeting efficiency constraints imposed by wireless hardware limitations.