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Dr. Qi Zhang is a 2025–26 CAST Research Fellow and an associate professor in the School of Information Technology, leading transformative research to improve pediatric heart diagnostics using artificial intelligence. His work focuses on an AI-assisted system that supports quicker and more equitable pediatric cardiac care.  

“Most AI tools in cardiology are built for adults, yet children’s hearts develop differently and deserve their own technologies. I was drawn to the challenge of designing AI that truly serves young patients,” Zhang said. “Seeing how early diagnosis shapes a child’s life makes this work deeply personal for me.” 

Project overview 

This project focuses on data online. It turns complex ECG data into clear, shareable insights that support better and more equitable care. 

Zhang mentioned that partnerships with OSF HealthCare and Illinois State University kept the project grounded, providing all the necessary elements. OSF provides pediatric data and medical expertise, while Illinois State supplies high-performance computing, AI expertise, and student researchers who advance the technical work.  

“Together, we bridge the gap between engineering and medicine, translating laboratory research into practical tools that can improve patient care,” Zhang said. 

“Together, we bridge the gap between engineering and medicine, translating laboratory research into practical tools that can improve patient care.”

Dr. Qi Zhang

Student involvement 

Zhang mentioned that Illinois State students are integral to the project’s success. Under his mentorship, they contribute to his research by developing code modules, analyzing cardiac data, and designing visualization tools that make complex medical information easier to interpret. 

“Students’ participation not only strengthens the project’s outcomes but also provides them with valuable, hands-on experience in applying artificial intelligence to real-world health care challenges,” Zhang noted.

“Students’ participation not only strengthens the project’s outcomes but also provides them with valuable, hands-on experience in applying artificial intelligence to real-world healthcare challenges.”

Dr. Qi Zhang

Challenges and solutions 

According to Zhang, developing AI and telemedicine tools for children presents unique challenges in terms of data accuracy and secure data sharing. Zhang and his team focus on refining data quality through collaboration with clinicians and implementing encrypted communication systems that ensure patient privacy. 

Moreover, their work also addresses health care inequality. Many rural families live hours away from pediatric specialists. The AI system will enable local doctors to securely share ECG data with experts, thereby helping to shorten diagnosis times and improve outcomes. “Our goal is to make advanced cardiac care more accessible to every child, regardless of where they live,” Zhang said.

“Our goal is to make advanced cardiac care more accessible to every child, regardless of where they live.”

Dr. Qi Zhang

Zhang’s team plans to expand the system to include real-time heart monitoring and early risk prediction for pediatric heart conditions. Future phases will integrate wearable sensors and imaging data to build a broader platform that supports both diagnosis and clinician training. 

Support from CAST 

According to Zhang, the College of Applied Science and Technology research fellowship and internal funding have given him the time and support to innovate while mentoring students who share a passion for applying AI for social good.

“The support from CAST has allowed me to turn technical concepts into meaningful initiatives that benefit both patients and students,” Zhang said. 

Through innovation, collaboration, and mentorship, Zhang’s research reflects his commitment to advancing technology and improving lives by transforming the future of pediatric cardiology.

Learn more about the College of Applied Science and Technology.

As artificial intelligence becomes more powerful and widespread, so does the environmental cost of running it.

Behind every chatbot, image generator, and television streaming recommendation are massive banks of millions of computers housed in an increasing number of data centers that consume staggering amounts of electricity and water to keep their machines cool. Most of that electricity is still produced by fossil fuel-burning power plants, which contribute directly to air pollution and climate change.

A study from UC Riverside’s Marlan and Rosemary Bourns College of Engineering, however, proposes a solution to this growing problem. It outlines a method to dramatically reduce the pollution caused by AI processing in large data centers—while also extending the life of the hardware doing the work. No existing system combines these two goals, say the authors, professors Mihri Ozkan and Cengiz Ozkan. 

While other strategies focus mainly on scheduling computing tasks when or where electricity is cleaner, the proposed system goes further. Called the Federated Carbon Intelligence, or FCI, it integrates environmental awareness with real-time assessments of the condition of the servers in use. The goal is not just to minimize carbon emissions but also to reduce the stress and wear and tear on the machines that generate the pollution.

The researchers, who are married, backed their proposal with simulations. Their modeling showed that FCI could reduce carbon dioxide emissions by up to 45 percent over a five-year period. The system could also extend the operational life of a server fleet by 1.6 years.

“Our results show that sustainability in AI cannot be achieved by focusing on clean energy alone,” said Mihri Ozkan, professor of electrical and computer engineering. “AI systems age, they heat up, and their efficiency changes over time—and these shifts have a measurable carbon cost.

“By integrating real-time hardware health with carbon-intensity data, our framework learns how to route AI workloads in a way that cuts emissions while protecting the long-term reliability of the machines themselves.”

By constantly monitoring the temperature, age, and physical wear of servers, FCI helps avoid overworking machines that are already stressed or nearing the end of their useful life. In doing so, it prevents costly breakdowns, reduces the need for energy and water-intensive cooling, and keeps servers running longer. 

This approach recognizes that sustainability isn’t just about cleaner energy. It’s also about getting the most out of the hardware we already have, the authors say.

Their system further accounts for the complete lifecycle carbon footprint of computing—especially the embodied emissions from manufacturing new servers. By keeping existing machines in service longer and distributing computing tasks in a way that balances performance, wear, and environmental impact, the system addresses both sides of the sustainability equation.

“We reduce operational emissions in real time, but we also slow down hardware degradation,” said Cengiz Ozkan, professor of mechanical engineering. “By preventing unnecessary wear, we reduce not only the energy used today but also the environmental footprint of tomorrow’s hardware production.”

FCI dynamically determines where and when to process AI tasks based on constantly updated data. It tracks the condition of the machines, gauges the carbon intensity of electricity at any given time and place, and evaluates the demands of each AI workload. Then, using that information, it makes real-time decisions to send the task to the server best suited to handle it—with the least impact on the planet and the machine.

Deploying such systems—driven by AI models—could represent a major advancement for both environmental sustainability and the cloud computing industry, the researchers said.

Establishing the adaptive framework would not require new equipment, just smarter coordination across the systems already in place, Mihri Ozkan said.

Published in the journal MRS Energy and Sustainability, the study is titled “Federated carbon intelligence for sustainable AI: Real-time optimization across heterogeneous hardware fleets.”

The researchers say the next step is partnering with cloud providers to test FCI in real data centers, a move that could lay the foundation for NetZero-aligned AI infrastructure worldwide. The Ozkans described an urgent need. The growing number of data centers is already consuming more power than entire countries, including Sweden.

“AI is expanding faster than the energy systems that support it,” Cengiz Ozkan said. “Frameworks like ours show that climate-aligned computing is achievable—without sacrificing performance.”