Paul Ahavi

This paper develops growth-coupled metabolic biosensors for disease detection and prognosis using COVID-19 as a proof-of-concept, achieving high diagnostic and prognostic accuracy.

This paper demonstrates using E. coli as a living reservoir computer to classify COVID-19 plasma samples and perform nonlinear computations through growth responses.

dAMN predicts bacterial growth dynamics using a hybrid neural-mechanistic model, achieving high accuracy across diverse nutrient environments and capturing lag phase adaptation.