Professor Anne Andrews of the University of California, Los Angeles (UCLA) discussed measuring neurotransmitters in the brain using voltammetry and machine learning. Andrews explained that her group uses Bayesian optimization to design new voltammetry waveforms and pattern-recognition models to separate overlapping electrochemical signals, aiming to measure multiple neurotransmitters at once. She also discussed her lab’s publicly available software and its potential for translating neurochemical monitoring from research settings toward real-world use. Andrews described her early inspiration from chemistry teachers, earning an undergraduate degree in chemistry, working in analytical chemistry, and taking a one-year temporary appointment at the National Institute of Mental Health, where she fell in love with neuroscience. Regarding serotonin and dopamine, she noted they are implicated in normal and disordered behavior, with depression and anxiety associated with serotonin, and substance use and movement disorders associated with dopamine, while also working together. On measurement methods, she explained that researchers initially measured concentrations in brain tissue ex vivo, then developed methods for in vivo real-time monitoring. The two widely employed methods are microdialysis, which involves implanting a semi-permeable membrane into the brain to sample extracellular space, and voltammetry, both practiced in her lab.