End-to-End Uncertainty Quantification

Collaborators:

• Dr. Natalia Trayanova, Johns Hopkins University
• Dr. Shantenu Jha, Rutgers University

Mathematical models of a living system are always subject to epistemic uncertainties that represent our limited knowledge about a system. While personalized models have shown increasing potential in medicine, their uncertainties remain the main roadblock to their widespread adoption in the healthcare industry. Existing efforts in uncertainty quantification (UQ) mostly investigated how generic variability in a model element – represented by probabilistic distributions defined a priori – results in output variability. However, a personalized model first has to be customized from patient-specific data, before being able to make predictions pertinent to that individual. Therefore, the uncertainty in a personalized model is not generic, but driven by data used to customize the model. This poses a unique challenge: to measure the variability in patient-specific predictions, we must first infer the uncertainty within the data-driven model elements, before propagating this uncertainty to model predictions. This unmet need – which we term as end-to-end UQ – is the focus of our research.

In specific, we are interested in adopting and advancing Bayesian active learning for surrogate modeling that is focused on the posterior support of the model uncertainty, formulating hybrid MCMC methods accelerated by the surrogate model, and unified end-to-end propagation of the uncertainty from the input data through the model to the model predictions.

Student members: Jwala Dhamala, Ankit Aich