Two faculty members in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee's Tickle College of Engineering have received National Science Foundation CAREER awards totaling nearly $1.2 million. The five-year grants will fund separate projects aimed at reimagining superconducting logic systems and building low-cost AI devices that communities can train themselves.
Ahmedullah Aziz will receive $550,000 to develop the fundamentals of a new generation of superconducting electronics. Sai Swaminathan was awarded more than $638,000 to create a palm-sized device that puts trainable AI into the hands of community members without requiring internet access.
Reimagining superconducting logic
Aziz's work targets a core limitation in current superconducting computing hardware. These systems process, route, and store digital information at ultra-low temperatures using superconducting devices rather than room-temperature semiconductor transistors. They are exceptionally fast and consume very little energy, but they lack important functionalities that would make them practical for broader use.
"My project focuses on making superconducting logic systems more functional, programmable and scalable, with better 'control knobs' and built-in memory," Aziz said. His research group will develop predictive device models, design and evaluate new circuits and larger computing architectures, and fabricate and test prototypes to validate the underlying concepts.
The resulting technologies address two pressing needs. First, they help tackle the rising energy demand of AI for Science & Research infrastructure. Second, the superconducting logic systems will support the development of highly efficient larger-scale quantum computers by bringing more control, processing, and memory functions into the cryogenic environment where quantum processors operate.
Aziz credited colleagues, mentors, family, and students for the opportunity. The funding will support a complete research pipeline, including graduate and undergraduate students who carry out the work. He also plans to translate certain project elements into hands-on activities for high school students and teachers, letting them explore superconducting devices without access to a cryogenic laboratory.
AI that communities can train themselves
Swaminathan's project takes a different approach to computing's frontiers. Many Tennesseans already use smart devices like thermostats, speakers, and fitness trackers that run pre-trained AI models. When one of those models fails, users cannot repair it. When a new situation arises, they cannot teach the device to handle it.
Swaminathan, his students, and community partners will develop AI devices that users can train to answer questions that matter locally. Each device will include a low-powered computer, a sensor such as a camera or microphone, and a simple user interface with a touchscreen, dials, or other physical controls. They will work without internet access.
"Imagine the benefits AI can have for communities if the technology is designed with communities," Swaminathan said. His team has begun working with organizations across Appalachian Tennessee to understand regional challenges like food security, water quality, and care for older adults. "These organizations have local relationships and understandings, but they're often small or stretched thin. Once community members can build and train models by pressing just a few buttons, nonprofits can deploy hundreds of these devices to augment their capacity to achieve greater impacts."
He described two potential applications. The Knoxville-based organization Socially Equal Energy Efficient Development could use the devices to train local youth to monitor soil health in its community garden. Volunteers with Clean Water Expected in East Tennessee could use them to track water pollutants during river cleanups.
First, Swaminathan's team must overcome a major technical hurdle: fitting AI models, which are typically quite large, onto devices with limited memory and processing power. Some will have less memory than a single photo on a phone. His students will then lead workshops with community members to co-design functionality and user interfaces. The approach aligns with an AI Learning Path for Research Scientists focused on practical, deployable systems.
Why this matters for science and research professionals
The two CAREER awards signal NSF investment in computing research that spans from the ultra-cold physics of superconducting logic to the hands-on challenge of making AI trainable on a palm-sized device without cloud connectivity. For researchers in adjacent fields, Aziz's work on predictive device models and prototype fabrication could yield new tools for cryogenic computing architectures. Swaminathan's emphasis on co-design with end users offers a template for bridging the gap between model compression research and real-world deployment in resource-constrained settings. Both projects will produce open questions - and open opportunities - for graduate students and collaborators over the five-year grant period.
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