Hamid Motallebzadeh

The paper introduces a Composite Certainty Framework to address metric degeneracy in parameter inference by aggregating multiple uncertainty metrics into a single consensus score, revealing parameter-specific identifiability profiles and clinical thresholds.

The paper develops a multi-metric framework to assess spectral biofidelity, showing that no single metric reliably captures all types of spectral distortion and that consensus across metrics improves validation and machine learning loss design.

This paper uses simulation-based inference to tune a finite-element model of the middle ear, providing probability distributions of parameters that reproduce experimental data and enabling objective diagnosis of hearing loss.