Applied Research

Artificial Intelligence meets the brain – using AI for deeper insight into brain health.

Our daily experience with the world is multimodal. We see things. We feel textures. We hear noises, smell odors and taste foods — all to shape our understanding of the world. But most data on the human brain is siloed and not connected. Our I-BRAIN Science Team has built a one-of-a-kind data and human brain tissue repository with multiple sources of data integrated into one system, to understand and develop new treatments for human disorders and improve clinical care.

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The WISDOM science team will use data from Chicago hospitals to develop artificial intelligence technologies that improve patient outcomes.

The extraordinary team of clinicians, informaticians and engineers from UIC, UIUC and Rush are starting with data from cancer patients from underserved populations — seeking patterns and insights that will change the state of the practice.

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The science and technology of building robust and trustworthy AI-based systems will be one of the greatest challenges of our time.

Machines that learn from large amounts of data can mimic human intelligence to a large degree but are inherently fragile. Our research areas include autonomous vehicles and drones, robots in manufacturing and assisted living, robotic surgery, and AI applications in finance that call for privacy, fairness and ease of use.

This science team’s ambitions are to build an innovation center dedicated to advanced technology in the U.S. manufacturing, construction and agriculture industries.

Application areas include automated construction vehicles, cooperative autonomy for manufacturing, digital autonomous agriculture, additive manufacturing, and supply chain logistics, to name a few.

This science team is creating a non-invasive, cost-effective way to examine community spread of COVID-19, by measuring the virus in human fecal matter in wastewater.

Such monitoring, known as wastewater-based epidemiology, should warn of emerging viral hotspots up to a week earlier than traditional tests, and give public health workers valuable time to mobilize.
The program, however, will be important for additional and future public health concerns.

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The construction industry is ripe for transformation: inefficient, unsafe, and unpredictable. But digital technologies are showing promise.

Construction problems expose new challenges for AI, and this science team will look at solutions including image-based 3D reconstruction of interiors, 3D pose estimation, and semi-structured text analysis. Further AI methodologies will provide solutions in finance, insurance, and transportation logistics.

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Today, system designers focus on functionality and performance requirements, often adding security as an afterthought and ignoring privacy altogether.

Many view such privacy concerns as secondary and accepted their violation as a necessary byproduct of security. This science team will combine techniques drawn from cryptography, machine learning, software engineering and trustworthy computing to obtain practical, usable and scalable systems that will provide consumers with the level of privacy that they desire.

This science team will drive research, education, and outreach for autonomous systems in agriculture.

The ultimate goal is to improve Illinois farmers’ productivity, sustainability and profitability. We will advance AI in three areas: (1) robots to collect data for under-canopy crops, utilizing weeding and spraying robots; (2) urban food garden robots; (3) plant manipulation in berry-nut orchards with low-cost mobile robots and soft manipulators.

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Chronic kidney disease (CKD) affects approximately 15% of the U.S. population and Medicare costs exceed $120 billion annually.

This science team will develop strategies to improve the health and wellness of CKD patients while realizing tremendous economic impact. Our vision is to optimize the healthcare experience and quality of life (QOL) of individuals with CKD. Technology plays a key role, specifically using VR to increase exercise adherence and nutrition knowledge, and using machine learning to optimize patient-centered fluid management.