J. Nathan Kutz

The paper introduces a stochastic dynamical model to capture bistable dynamics during sleep onset and shows its parameters correlate with subjective sleepiness reports.

The paper uses a multiparent yeast population to identify QTL for growth rates across environments, revealing context-dependent genetic architectures and allelic heterogeneity.

The paper demonstrates that a single arousal measurement can reconstruct spatiotemporal brain dynamics across multiple modalities in awake mice.

The paper introduces SHRED, a shallow recurrent decoder that maps neural population dynamics to individual neurons and behavior using limited sensor data across multiple organisms.

This paper develops a deep learning method to reconstruct dense human motion data from sparse sensors, enabling accurate tracking of unmonitored body segments and improving digital biomarker estimates.

The paper predicts the functional effect of compound heterozygous genotypes using a yeast-based assay and a linear model applied to variant effect maps.

The paper develops a high-throughput functional assay to assess the impact of 1,570 OTC missense variants, improving variant interpretation using ACMG/AMP guidelines.

This study uses neural networks to quantify individual gait responses to ankle exoskeletons by modeling discrepancies between nominal and exoskeleton gait data.

This paper introduces Non-Stationary Dynamic Mode Decomposition (NS-DMD) to analyze time-varying spatiotemporal data, demonstrating its application to neural recordings from a non-human primate.

This paper introduces a dynamic mode decomposition method to extract time-resolved resting state networks from fMRI data, showing improved reproducibility and individualized structure.