The Oliveira Lab develops AI-enabled experimental platforms to understand and engineer microbial communities across space and time. We combine synthetic biology, long-term live-cell imaging, microfluidics, automation, and machine learning to generate controlled, spatially resolved biological data and uncover design principles for collective behavior.

​

An important goal is to move from observation to closed-loop discovery. Predictive models and digital twins help select informative experiments, identify effective perturbations, and guide biological systems toward desired states. These approaches support applications in biosensing, biomanufacturing, health, agriculture, and environmental monitoring.

​

Our work integrates massively parallel microscopy, automated microfluidic screening, computer vision, control theory, dynamical modeling, and design optimization. Emerging directions include autonomous discovery platforms and AI-guided bioreactor networks connecting biological measurements, models, and interventions in real time.