Michael U. Gutmann

This paper develops a risk-averse optimization framework combining Bayesian inference and Thompson sampling to design robust genetic circuits despite model inaccuracies and parameter uncertainty.

This study compares ranking aggregation methods for gene lists using simulated and real data to evaluate their performance in meta-analyses.

The paper introduces a synthetic model-based approach for uncertainty quantification in stochastic gene expression models, showing improved performance over existing methods.

The paper identifies four distinct endotypes in acute pancreatitis using multiomic data and clinical metrics, showing diverse molecular and systemic responses.

This paper uses Approximate Bayesian Computation to improve parameter estimation in stochastic birth-death models for disease outbreaks, showing better identifiability of key parameters like R and infectious population size.

The paper assesses the identifiability of a tuberculosis transmission model using IS6110 fingerprinting data, showing that key parameters like reproductive value cannot be reliably inferred from the data.