We study the design and function of networks of neurons, networks of brains, and other (mostly biological) networks - asking how they represent and process information, develop, learn, and make decisions.

We combine theoretical work, modeling, analysis of (lots of) data, and behavioral experiments, in studying neural encoding and decoding in large neural populations, neural circuit architecture, deep and shallow neural networks, collective behavior in animal groups and artificial agents, noise and information in biological systems, learning, and decision making.

Exploring questions at the intersection of biology, physics, computation, and learning - we rely on our collective backgrounds (physics, computer science, biology, engineering, psychology, and math), to mix, fuse, and apply ideas and tools from machine learning, statistical physics, information theory, network theory, applied math, and karate.