What defines an expert surgeon? Does a surgical expert possess finer motor skills than a resident or novice? Can surgical performance be objectively measured to improve outcomes? Objectively assessing surgical expertise could help address over 300,000 annual perioperative mortalities and their associated costs. Current subjective methods often fail to reliably distinguish expert surgeons from novices, highlighting the need for a data-driven approach.

Medical errors, including surgical errors, collectively rank as the third leading cause of death in the U.S., following cancer and cardiovascular disease. At our center, we are advancing physics-based simulation technologies and developing novel AI paradigms grounded in medical imaging to reduce medical errors. These innovations provide platforms for assessing surgical and emergency medicine skills as well as for pre-operative treatment planning.

With the surge of probabilistic modeling, more and more branches of science subscribe to the use of Bayesian models for inference and uncertainty quantification. In recent years, deep learning has proven indispensable for scaling up Bayesian inference to challenging inverse problems in the life sciences. One such problem is determining the hidden parameters of complex, biophysically detailed models from high-dimensional observations, such as neuroimaging or microscopy data.