Trevor Bekolay’s primary research interest is in learning and memory. In his Master’s degree, he explored how to do supervised, unsupervised, and reinforcement learning in networks of biologically plausible spiking neurons. In his PhD, he applied this knowledge to the domain of speech to explore how sounds coming into the ear become high-level linguistic representations, and how those representations become sequences of vocal tract movements that produce speech. Trevor is also passionate about reproducible science, particularly when complex software pipelines are involved. In 2013, he started a development effort to reimplement the Nengo neural simulator from scratch in Python, which has now grown to a project with over 20 contributors around the world.