headshot of Pete Gueldner

Pete Gueldner

Pre-Doctoral Fellow Certificate
Pete.Gueldner@pitt.edu
Personal Research Website Bioengineering Department

overview

Pete Gueldner is a doctoral candidate at the University of Pittsburgh. He is studying Bioengineering with a concentration in biomechanics. His research interests are related to vascular biomechanics and artificial intelligence, primarily investigating aortic aneurysms. He is currently developing a novel bubble inflation testing apparatus to mechanically characterize diseased aneurysmal tissue. More rigorous experimental testing tools can greatly improve computational models by more properly analyzing material properties and failure properties. He designs software tools that aid in automation of image analysis in both experimental and computational techniques. In addition to his work in experimental mechanics, he is also interested in how artificial intelligence tools can aid computational methods and clinical diagnosis/prognosis of aneurysms.

about

B.S., University of Texas at San Antonio, 2016 - 2020

Chung, T.K., Gueldner, P., Kottakota, A., Hangey, C., Lee, J., Liang, N.L., & Vorp, D.A. (2025). Augmented reality visualization of biomechanical wall stresses on abdominal aortic aneurysms using artificial intelligence. Science Talks, 13(Circulation 56 1977), 100432.Elsevier. doi: 10.1016/j.sctalk.2025.100432.

Gueldner, P.H., Kerr, K.E., Liang, N., Chung, T.K., Tallarita, T., Wildenberg, J., Beckermann, J., Vorp, D.A., & Sen, I. (2025). Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A Case Study. Journal of Vascular Surgery Cases and Innovative Techniques, 101806.Elsevier. doi: 10.1016/j.jvscit.2025.101806.

Chung, T.K., Gueldner, P.H., Aloziem, O.U., Liang, N.L., & Vorp, D.A. (2024). An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes. Sci Rep, 14(1), 3390.Springer Nature. doi: 10.1038/s41598-024-53459-5.

Chung, T.K., Kim, J., Gueldner, P.H., Vorp, D.A., & Raghavan, M.L. (2024). A Comparative Study of Machine Learning and Algorithmic Approaches to Automatically Identify the Yield Point in Normal and Aneurysmal Human Aortic Tissues. J Biomech Eng, 146(4), 044503.ASME International. doi: 10.1115/1.4064365.

Gueldner, P.H., Darvish, C.J., Chickanosky, I.K.M., Ahlgren, E.E., Fortunato, R., Chung, T.K., Rajagopal, K., Benjamin, C.C., Maiti, S., Rajagopal, K.R., & Vorp, D.A. (2024). Aortic tissue stiffness and tensile strength are correlated with density changes following proteolytic treatment. J Biomech, 172, 112226.Elsevier. doi: 10.1016/j.jbiomech.2024.112226.

Gueldner, P.H., Marini, A.X., Li, B., Darvish, C.J., Chung, T.K., Weinbaum, J.S., Curci, J.A., & Vorp, D.A. (2023). Mechanical and matrix effects of short and long-duration exposure to beta-aminopropionitrile in elastase-induced model abdominal aortic aneurysm in mice. JVS Vasc Sci, 4, 100098.Elsevier. doi: 10.1016/j.jvssci.2023.100098.

Chung, T.K., Gueldner, P.H., Kickliter, T.M., Liang, N.L., & Vorp, D.A. (2022). An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms. Bioengineering (Basel), 9(11), 601.MDPI. doi: 10.3390/bioengineering9110601.

Pillalamarri, N.R., Patnaik, S.S., Piskin, S., Gueldner, P., & Finol, E.A. (2021). Ex Vivo Regional Mechanical Characterization of Porcine Pulmonary Arteries. Exp Mech, 61(1), 285-303.Springer Nature. doi: 10.1007/s11340-020-00678-2.