Ekaterina Krymova

Calibrated uncertainty quantification in deep scientific surrogates no longer requires expensive outer-loop sweeps: embedding empirical Bayes directly into heteroscedastic neural Gaussian processes delivers calibrated predictions at dramatically reduced compute.

Identifying distinct disease trajectories in T2DM reveals critical insights into patient progression and comorbidity risk that could transform clinical management.

Continuous modeling of ventricular motion can enhance heart failure risk prediction, outperforming traditional cardiac markers.