Brain stimulation is an important tool for clinical and scientific research, but the large search space of stimulation parameters often makes it infeasible to test every parameter combination. In this scenario, creating a model that maps the stimulation parameters onto the brain’s response can be beneficial. Training such an expansive model usually requires more stimulation-response samples than can be collected in a given experimental session. To address these challenges, we propose MiSO (MicroStimulation Optimization), a closed-loop stimulation framework to drive neural population activity toward specified states by optimizing over a large stimulation parameter space. MiSO leverages two key innovations: 1) performing latent space alignment to create a large training data set by statistically merging stimulation-response samples across sessions, and 2) using a convolutional neural network (CNN) to predict the brain's response to unseen stimulation parameters. We tested MiSO in closed-loop experiments using electrical microstimulation in the prefrontal cortex of a non-human primate. Guided by the CNN predictions, MiSO successfully searched amongst thousands of stimulation parameter configurations to drive the neural population activity toward specified states. More broadly, MiSO increases the clinical viability of neuromodulation technologies by enabling the use of many-fold larger stimulation spaces.