A College of Engineering master’s student has earned the HiPerGator Early Career Award for his contributions to artificial intelligence research through the Florida Institute for National Security (FINS).
Matheus Maldaner, who studies AI Systems in the Department of Engineering Education, is the only master’s student to receive the award. Presented by UF IT’s Research Computing division during the AI awards ceremony, the HiPerGator Early Career Award recognizes early-career researchers who make significant contributions to artificial intelligence using UF’s HiPerGator supercomputer.
Maldaner, a first-year master’s student, originally graduated from the University of Florida summa cum laude with a bachelor’s degree in data science. Born in Brazil, he moved to Florida in 2017 and went on to earn some of UF’s highest student honors, including induction into the UF Hall of Fame and the Outstanding Student Leader Award.
His research with FINS focuses on developing more transparent and explainable artificial intelligence systems.
“This award belongs to me just as much as it belongs to every professor, mentor and friend who has helped shape me,” Maldaner said. “It reinforces the conviction that I am on the right track.”
Researching Neurosymbolic AI at FINS, Maldaner was awarded for his work on differentiable logic gate networks. His work is designed to help researchers better understand how artificial intelligence systems reach their conclusions.
Artificial intelligence systems, such as large language models, operate as “black boxes,” where users can see the input and the output but not the reasoning in between. While effective in some applications, this lack of transparency can pose challenges in fields such as healthcare, judicial systems and national security.
Maldaner’s research also addresses another critical limitation of many modern artificial intelligence systems: speed. The use of differentiable logic gates aims to improve the speed and efficiency of the inference process used in AI systems. This allows AI systems to make decisions much faster.
“Understanding why a system makes a decision is sometimes more important than achieving perfect accuracy,” Maldaner said. “These models are designed for situations where speed and transparency matter.”
That speed is especially important for real-time technologies that operate outside of large data centers, such as wearable devices, driver monitoring systems and other edge technologies. In these settings, AI must process information instantly, often with limited computing resources and without relying on cloud-based systems. Faster, more interpretable models can help ensure these technologies respond quickly and predictably, a crucial factor in applications where delays or unexplained decisions could impact safety or user trust.
Matheus credits much of his success to his mentors Stephen Wormald, Domenic Forte,, Ph.D., and Damon L. Woodard, Ph.D., professor and director of FINS.
Woodard said Maldaner’s research demonstrates a careful balance between technical performance and real-world accountability.
“Matheus’s research on differentiable logic gates tackles a core challenge in modern AI by showing how intelligent systems can be both powerful and understandable,” Woodard said. “The depth and maturity of his work at such an early stage in his career signal exceptional promise and strongly justify his selection for this award.”
As Maldaner continues his graduate studies at UF, he plans to further develop AI systems that prioritize transparency, speed and real-world reliability, advancing research that supports both innovation and responsible technology use.