Inference and dynamics using clinical data for mathematical physiology and biomedicine
David Albers, University of Colorado
Tuesday February 9, 12-1pm
Zoom Link: https:/mcgill.zoom.us/j/91589192037
Abstract: The talk will focus on dynamics and inference problems in biomedicine using real data collected in clinical settings. Inference complexities, successes and failures, primarily in the context of applying data assimilation to infer ordinary differential equations, will be discussed. In particular, the talk will focus on inference given severe data sparsity, how the dynamics-inference mismatch problem, how rank-one dynamics induce important and surprising uncertainty (delay-induced uncertainty), and some related open and solved problems. The talk will also focus on realism related to the modeling and data measurement context, including modeling that does not ignore important complexities of the clinical environment, how to get at important problems and realities, and deployment of models for generating actionable knowledge. The particular areas of focus will be the glucose-insulin system and pulmonary or lung mechanics.