Adrian Buganza Tepole

The Tepole Lab investigates how the mechanical form and function of living systems emerge across scales, from modeling cell mechanobiology and regulatory networkS, to the tissue level mechanical behavior. We develop new mathematical models and numerical methods, including new machine learning tools and uncertainty quantification frameworks, in order to capture the unique multi-scale multi-field phenomena of living tissues. We apply our tools to relevant clinical scenarios such as personalized treatment planning of breast cancer patients.

Research Interests

Computational mechanobiology, Soft tissue mechanics, Data-driven solid mechanics, Breast cancer biomechanics, Machine learning methods in computational mechanics, Uncertainty quantification methods in computational mechanics, Reconstructive surgery, Skin

Unlike structural materials, tissues actively adapt to their environment by signaling networks between mechanical cues, cells, and chemicals across several temporal and spatial scales. The fundamental interplay between form and function, from the cell to the tissue level, gives rise to the complex mechanical behavior and adaptive capabilities of living tissue. Modeling, experimentation, and computation can, on the one hand, help us understand the fundamental mechanisms underlying tissue adaptation, while ultimately generating inexpensive technologies to improve healthcare. Our research uses the theory of mechanics and computational systems biology to characterize tissue response. To enable application to relevant medical problems, we are pioneering simulation and machine learning tools that can accelerate inference and prediction under the uncertainty inherent to population variability and available clinical data.