I am founder and chief scientist at Neurotaxis, a neuroscientific consulting organization focused on computational modeling and data analysis.

I am interested in how the brain processes the complex streams of sensory inputs we experience in day-to-day life. How are such inputs integrated over time to support memory-based computational, behavioral, and learning processes? I am also interested in how macroscopic computations emerge from strongly interacting microscopic processes. For instance, how can network models produce brain-like chaos while also robustly retaining information in working memory? 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.

I was previously a postdoctoral researcher at the Princeton Neuroscience Institute and the Center for the Physics of Biological Function, where I developed thereotical models for the neural basis of working and episodic memory, as well as methods to compare neural computations against natural behavior data. 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.