UW Electrical Engineering Student Awarded Best Paper at International Conference

Tessa Livingston came to the University of Wyoming determined to make a meaningful impact on people's lives, and her research in the Department of Electrical Engineering and Computer Science stands to do just that.

Livingston, a master's student from Lander studying electrical engineering, recently was recognized at the 11th World Congress on Electrical Engineering and Computer Systems and Science (EECSS 2025) for her paper titled "Synthetic Data Generation of Surgical Drills using Physics-Constrained GAN: Preliminary Results."

The judges at EECSS 2025 recognized Livingston not only for the quality of her paper and associated presentation at the Congress, but also for the innovation, clarity and potential for real-world impact of the research itself.

Livingston came to UW with a background in health care and family expertise in orthopedic surgery, so it comes as no surprise that her research capitalizes on the intersection of her interests in machine learning and generative artificial intelligence (AI), and the health care sector.

The paper she presented at EECSS concerns a novel approach to using machine learning to improve the accuracy of the drilling procedure used during orthopedic surgery. Inaccuracy while drilling through bone is a major contributor to complications from surgery. One such complication is a distal radius fracture, a common fracture among pediatric and elderly patients that requires guiding a bone drill through multiple layers of bone without impacting underlying soft tissue.

However, different properties of those layers of bone have made precise real-time assessments of drilling depth difficult. Livingston's research, under the guidance of her adviser, Suresh Muknahallipatna, a UW professor of electrical engineering and computer science, innovated a novel approach to estimating drill timing and depth by incorporating physics-informed constraints into Generative Adversarial Networks (GANs), a prominent machine learning framework used in generative AI.

Judges were so impressed by the promise of Livingston's work that they encouraged her to explore broader applications of the proposed generative approach beyond orthopedic surgery, as it has strong potential for making a critical difference in other areas of health care, as well as in the fields of robotics, industrial systems and environmental monitoring.

While this is not Livingston's first professional publication, it is the first paper she has written on her own applied research.

"This experience taught me how essential it is to communicate research in a way that enables others to build upon it," she says. "Scientific progress is inherently collaborative, and the value of research lies in how effectively it contributes to the broader body of knowledge."

Livingston is on track to complete her master's degree this winter and plans to pursue a career in machine learning or quantum computing.

"My goal is to apply advanced computational methods to solve problems that directly improves people's lives, whether in health care, sustainability or intelligent systems," she says.

Livingston credits Muknahallipatna with helping her reach these goals.

"His support and insight were instrumental in the success of this research and in my growth as a researcher," she says.