Dr. Karli Gillette, Assistant Professor of Biomedical Engineering along with University of Utah Faculty, Dr. Akil Narayan and Dr. Ravi Ranjan, received a half a million dollar grant to be utilized over the next three years. The grant will go towards enhancing Digital twin technologies.
Digital twin (DT) technologies are rapidly becoming a cornerstone of modern engineering. From aerospace to manufacturing to healthcare, DTs offer a powerful way to simulate, monitor, and optimize complex systems by creating virtual replicas that evolve in real time with data. But as these systems grow in sophistication, so do the mathematical challenges behind them. At the heart of every digital twin lies a delicate balance between computational models and real-world data. Generating accurate models, calibrating them with noisy or incomplete data, and quantifying uncertainty are all tasks that demand advanced mathematical and statistical tools. While recent engineering advances have made DTs more accessible, the mathematical infrastructure needed to support them—especially in high-stakes applications like medicine—is still catching up.
This is where a new collaborative research project led by Dr. Karli Gillette and Dr. Akil Narayan, with support from cardiologist Dr. Ravi Ranjan and a network of international researchers, comes into play. Their work focuses on developing the mathematical and statistical foundations necessary to make digital twins more robust, scalable, and clinically relevant. The team is particularly interested in applying these tools to bioengineering through both cardiac digital twins (CDTs) and virtual heart populations (VHPs). CDTs are personalized models built from patient-specific clinical data, offering a glimpse into how an individual’s heart might respond to treatments or progress over time. VHPs, on the other hand, simulate heart function across populations, helping researchers understand disease mechanisms and predict population-level outcomes. Both applications present unique computational challenges—making them ideal testbeds for advancing digital twin methodologies.
By developing a comprehensive pipeline for CDT and VHP modeling, the team aims to demonstrate how mathematical innovation can drive practical impact in healthcare and beyond. Their work not only contributes to the growing field of digital twin science but also highlights the importance of interdisciplinary collaboration in solving complex engineering problems. Stay tuned as this project unfolds—bringing together mathematics, engineering, and medicine to shape the future of personalized healthcare.