Cell-to-cell heterogeneity in gene expression can elevate antibiotic resistance in one microbe while other cells remain susceptible. These transient forms of drug resistance are often stochastic and dynamic, leading to single-cell level differences in resistance that change with time. To date, methods for quantifying these effects have relied on careful observations of native expression patterns. In this talk, I will discuss a novel approach for controlling gene expression dynamics in single cells that can be used to precisely drive expression in thousands of cells in parallel. In support of this, I will discuss our recent advances in automated image processing of time-lapse microscopy data using deep learning models (DeLTA). Once trained, the DeLTA algorithm requires minimal input from the user and can rapidly segment, track, and reconstruct lineages for bacteria growing in microfluidic chips and on two dimensional surfaces. I will also discuss optogenetic control methods that allow us to use light-based feedback to regulate gene expression in real time. Using a combination of deep learning-based models and rapid image analysis, we can simultaneously control gene expression in thousands of cells in parallel. Together, these approaches offer powerful methods that can be used to quantify and control cell-to-cell heterogeneity in antibiotic resistance, providing a detailed view into strategies bacteria can use to evade drug treatment.