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The AI Journal: UF and other research universities will fuel AI. Here’s why

Alina Zare Ph.D.

Feb 2, 2026

3 min

In the global AI race between small and major competitors, established companies versus new players, and ubiquitous versus niche uses, the next giant leap isn’t about faster chips or improved algorithms. Where AI agents have already vacuumed up so much of the information on the internet, the next great uncertainty is where they’ll find the next trove of big data.

The answer is not in Silicon Valley. It’s all across the nation at our major research universities, which are key to maintaining global competitiveness against China.

To teach an AI system to “think” requires it to draw on massive amounts of data to build models. At a recent conference, Ilya Sutskever, the former chief scientist at OpenAI — the creator of ChatGPT — called data the “fossil fuel of AI.” Just as we will use up fossil fuels because they are not renewable, he said we are running out of new data to mine to keep fueling the gains in AI.

However, so much of this thinking assumes AI was created by private Silicon Valley start-ups and the like. AI’s history is actually deeply rooted in U.S. universities dating back to the 1940s, when early research laid the groundwork for the algorithms and tools used today. While the computing power to use those tools was created only recently, the foundation was laid after World War II, not in the private sector but at our universities.

Contrary to a “fossil fuel problem,” I believe AI has its own renewable fuel source: the data and expertise generated from our comprehensive public academic institutions. In fact, at the major AI conferences driving the field, most papers come from academic institutions. Our AI systems learn about our world only from the data we offer them.

Current AI models like ChatGPT are scraping information from some academic journal articles in open-access repositories, but there are enormous troves of untapped academic data that could be used to make all these models more meaningful. A way past data scarcity is to develop new AI methods that leverage all of our knowledge in all of its forms. Our research institutions have the varied expertise in all aspects of our society to do this.

Here’s just one example: We are creating the next generation of “digital twin” technology. Digital twins are virtual recreations of places or systems in our world. Using AI, we can develop digital twins that gather all of our data and knowledge about a system — whether a city, a community or even a person — in one place and allow users to ask “what if” questions.

The University of Florida, for example, is building a digital twin for the city of Jacksonville, which contains the profile of each building, elevation data throughout the city and even septic tank locations. The twin also embeds detailed state-of-the-art waterflow models. In that virtual world, we can test all sorts of ideas for improving Jacksonville’s hurricane evacuation planning and water quality before implementing them in the actual city.

As we continue to layer more data into the twin — real-time traffic information, scans of road conditions and more — our ability to deploy city resources will be more informed and driven by real-time actionable data and modeling. Using an AI system backed by this digital twin, city leaders could ask, “How would a new road in downtown Jacksonville impact evacuation times? How would the added road modify water runoff?” and so on.

The possibilities for this emerging area of AI are endless. We could create digital twins of humans to layer human biology knowledge with personalized medical histories and imaging scans to understand how individuals may respond to particular treatments.

Universities are also acquiring increasingly powerful supercomputers that are supercharging their innovations, such as the University of Florida’s HiPerGator, recently acquired from NVIDIA, which is being used for problems across all disciplines. Oregon State University and the University of Missouri, for example, are using their own access to supercomputers to advance marine science discoveries and improve elder care.

In short, to see the next big leap in AI, don’t immediately look to Silicon Valley. Start scanning the horizon for those research universities that have the computing horsepower and the unique ability to continually renew the data and knowledge that will supercharge the next big thing in AI.

Alina Zare

Professor

Alina Zare's research focuses on developing new machine learning and artificial intelligence algorithms to process data and imagery.

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Apr 1, 2026

3 min

UF develops breakthrough magnet that could transform metal production

Imagine if producing steel parts for agricultural equipment or even aluminum soda cans required only a fraction of the energy it does today. A University of Florida-led innovation may soon make this a reality. In a groundbreaking collaboration backed by a nearly $11 million federal grant, UF researchers have developed a first-of-its kind superconducting magnet that could advance metal production and position the United States as a global leader in alloy production.   “This revolutionary technology has the potential to substantially reduce the cost and energy use of heat treatments in the steel industry, and we are excited to help pave the way for its adoption in industry.” —Michael Tonks, Ph.D., UF’s interim chair of Materials Science and Engineering Funded by the U.S. Department of Energy’s Advanced Manufacturing Office, the project uses Induction-Coupled Thermomagnetic Processing, or ITMP, an advanced manufacturing method that integrates magnetic fields with high-temperature thermal processing. The national consortium of industry, academic and national laboratory partners is now led by Michael Tonks, Ph.D., UF’s interim chair of Materials Science and Engineering, who succeeded Michele Manuel, Ph.D., the project’s long-time leader. “This revolutionary technology has the potential to substantially reduce the cost and energy use of heat treatments in the steel industry, and we are excited to help pave the way for its adoption in industry,” said Tonks. It’s not just any piece of equipment; it’s a custom-built superconducting magnet with a unique ability to combine magnetic fields with high-temperature thermal processing. In partnership with the UF physics department, Oak Ridge National Laboratory, or ORNL, and six companies interested in the technology, the magnet and cylinder induction furnace now sit atop a 6-foot-high platform. The prototype, which costs more than $6 million to purchase and install, is capable of processing steel samples up to 5 inches in diameter making it a rare asset for academic research. Yang Yang, Ph.D., UF materials science research faculty member, estimated ITMP could reduce steel processing time by as much as 80 percent, cutting energy use and operational costs. “Thermomagnetic processing changes a material’s phase stability and kinetic properties, accelerating carbon diffusion in steel, said Yang. “Traditional furnaces cannot achieve these advanced material properties.” The system works by modifying the driving forces for important steel phase changes, which shortens heat treatment. “What normally takes eight hours can be done in just a few minutes.” Yang explained. “The magnetic field acts as an external driving force to make atoms diffuse faster.” Unlike conventional energy sources like electricity or natural gas, the ITMP process uses volumetric induction heating along with high-static magnetic fields to lower energy consumption. The project is still in a pilot phase and requires additional research and testing. At ORNL, researchers emphasized the rarity of UF’s prototype, citing its unprecedented combination of magnetic field strength and ability to process large samples and components. “This could significantly advance U.S. manufacturing and process efficiency for heat treatment of materials such as metal alloys of steel or aluminum,” said Michael Kesler, Ph.D., ORNL research scientist and lead collaborator. Kesler noted successful implementation of this technology could contribute to a reliable energy grid and more efficient industrial electrification. UF researchers contend it could also reduce carbon emissions, supporting cleaner, more sustainable manufacturing processes. The tall, two-level magnet now resides in the Powell Family Structures and Materials Laboratory on UF's East Campus. MSE plans to officially unveil it in December, inviting representatives from national labs, industry and academia. While Engineering students will have future opportunities to use it for research and experiential learning, UF researchers are optimistic about potential industry adoption for industrial manufacturing in the next five to 10 years. The award is part of a $187 million DOE initiative to strengthen competitiveness in U.S. manufacturing. If successful, the innovation could redefine how the world shapes the materials of tomorrow.

Mar 30, 2026

3 min

UF researchers aim to improve nutrition for cancer patients

A new study and first-of-its-kind food pharmacy at UF aim to help patients with cancer access and eat nutritious foods, giving them the best possible shot at a healthy future. As many as a third of cancer patients face food challenges, particularly in rural areas. Good nutrition can improve outcomes during and after treatment. With a grant from the Florida Department of Health, a team of researchers at the University of Florida Health Cancer Center and Sylvester Comprehensive Cancer Center will first assess the community’s nutritional needs. Then they’ll test the usefulness of a food-focused digital tool designed to connect patients to helpful resources. An on-site food pharmacy will help patients not only get the food they need to thrive but also provide tools for lasting change. “We’re taking a community-based approach to holistic cancer care,” said Dejana Braithwaite, Ph.D., associate director for population sciences at the UF Health Cancer Center. “Patients consistently express that nutrition is an important issue for them during cancer treatment. We want to address nutritional needs from treatment through survivorship with a sustainable intervention. ASCENT brings science and community together to make that a reality in Florida.” Braithwaite, a professor and division chief in the UF Department of Surgery, is leading the multi-institution study with Tracy Crane, Ph.D., R.D.N., director of lifestyle medicine, prevention and digital health and co-lead of the Cancer Control Program at Sylvester, part of the University of Miami Health System. Researchers from the UF colleges of Journalism, Medicine, and Public Health and Health Professions and UF/IFAS Extension are participating. The Florida Partnership for Adding Social Context to Address Cancer Survivorship Outcomes study, which the researchers have nicknamed ASCENT, will focus on those affected by the most prevalent cancers in Florida, including breast, lung, colorectal, prostate and blood cancers. “Cancer survivors who follow a healthy dietary pattern have a lower risk of recurrence and death,” said Cora Best, Ph.D., R.D.N., an assistant professor of nutritional sciences in the UF College of Agricultural and Life Sciences and study team member. “Some cancer therapies have long-term or late side effects that increase the risk for chronic conditions, like osteoporosis, which can be alleviated with good nutrition. That means a healthy diet during and after oncologic treatment can enhance lifespan and quality of life.” Researchers will start by conducting interviews with patients, providers and community-based organizations. They want to understand how to best use resources to meet the nutritional needs of those with cancer, such as food security and diet quality. “Community outreach and engagement with various groups is a cornerstone of the study,” said Francis Dalisay, Ph.D., an associate professor in the UF College of Journalism and Communications who helped develop the interview guides. The team will use the information to build a diet intervention with online surveys and patient navigator support, which they will test in a randomized clinical study at UF Health and Sylvester. Patient navigators will connect patients with resources like community programs or specialist referrals. The food pharmacy, located at the UF Clinical and Translational Sciences Metabolic Kitchen, will help cancer patients get healthy, whole nutrient-dense foods like high-protein items, fruits, vegetables and pantry staples. It will also provide workshops, personalized recipes and meal plans. Although the United States is a wealthy nation, food insecurity remains common, including in Florida, Best said. “The ASCENT study pairs evidence-based dietary guidance for cancer survivors with innovative strategies to overcome barriers like food insecurity,” she said. Ultimately, the study aims to empower patients so they can address lifestyle factors in their control, boosting their well-being. “I am hopeful this study will provide patients with appropriate resources to improve their overall nutrition, especially those who are malnourished,” said Paul Crispen, M.D., the Cancer Center’s associate director for clinical research and a study adviser.

Mar 27, 2026

3 min

New light-based chip boosts power efficiency of AI tasks 100 fold

A team of engineers has developed a new kind of computer chip that uses light instead of electricity to perform one of the most power-intensive parts of artificial intelligence — image recognition and similar pattern-finding tasks. Using light dramatically cuts the power needed to perform these tasks, with efficiency 10 or even 100 times that of current chips performing the same calculations. Using this approach could help rein in the enormous demand for electricity that is straining power grids and enable higher performance AI models and systems. This machine learning task, called “convolution,” is at the heart of how AI systems process pictures, videos and even language. Convolution operations currently require large amounts of computing resources and time. These new chips, though, use lasers and microscopic lenses fabricated onto circuit boards to perform convolutions with far less power and at faster speeds. In tests, the new chip successfully classified handwritten digits with about 98% accuracy, on par with traditional chips “Performing a key machine learning computation at near zero energy is a leap forward for future AI systems,” said study leader Volker J. Sorger, Ph.D., the Rhines Endowed Professor in Semiconductor Photonics at the University of Florida. “This is critical to keep scaling up AI capabilities in years to come.” “This is the first time anyone has put this type of optical computation on a chip and applied it to an AI neural network,” said Hangbo Yang, Ph.D., a research associate professor in Sorger’s group at UF and co-author of the study. Sorger’s team collaborated with researchers at UF’s Florida Semiconductor Institute, the University of California, Los Angeles and George Washington University on study. The team published their findings, which were supported by the Office of Naval Research, Sept. 8 in the journal Advanced Photonics The prototype chip uses two sets of miniature Fresnel lenses using standard manufacturing processes. These two-dimensional versions of the same lenses found in lighthouses are just a fraction of the width of a human hair. Machine learning data, such as from an image or other pattern-recognition tasks, are converted into laser light on-chip and passed through the lenses. The results are then converted back into a digital signal to complete the AI task. This lens-based convolution system is not only more computationally efficient, but it also reduces the computing time. Using light instead of electricity has other benefits, too. Sorger’s group designed a chip that could use different colored lasers to process multiple data streams in parallel. “We can have multiple wavelengths, or colors, of light passing through the lens at the same time,” Yang said. “That’s a key advantage of photonics.” Chip manufacturers, such as industry leader NVIDIA, already incorporate optical elements into other parts of their AI systems, which could make the addition of convolution lenses more seamless. “In the near future, chip-based optics will become a key part of every AI chip we use daily,” said Sorger, who is also deputy director for strategic initiatives at the Florida Semiconductor Institute. “And optical AI computing is next.”