I am founder and chief scientist at Neurotaxis, a consulting agency specializing in neural data science and computational modeling.

My research interests center around how the brain processes the complex information streams we experience in day-to-day life. How are such variable, yet structured inputs integrated over time to support memory-based computation, behavior, and learning? I am also interested in how macroscopic dynamics and computations emerge from strongly interacting microscopic processes. For instance, how can brain-like spiking chaos at the level of individual neurons power robust macroscopic computations? To address these questions I combine ideas and tools from a range of disciplines including dynamical systems theory, statistical physics, time-series analysis, coding theory, and high-dimensional computing.

Previously, I was a postdoctoral researcher at the Princeton Neuroscience Institute and the Center for the Physics of Biological Function, where I developed methods for inferring neural computations from natural behavior data and thereotical models for the neural basis of working and episodic memory. I have also been a teaching assistant for several computational neuroscience courses, including the Allen Institute’s Summer Workshop on the Dynamic Brain, the IBRO-Simons Computational Neuroscience Imbizo, and Coursera’s Computational Neuroscience course.

Check out some of the online tutorials I’ve made or my tips & tricks for improving your computational research experience.