Kian Jalaleddini

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.

The paper introduces an algorithm for detecting CBFV pulse onsets in TCD signals, achieving high accuracy with minimal false positives and negatives.

The paper develops an algorithm to identify and remove low-quality beats in TCD signals, improving the accuracy of TCD parameter estimation.