Fundamental gaps remain in our understanding of animal brains, especially human brains, in comparison with other organ systems in the body. One of these gaps is our very partial knowledge of the circuit architecture of brains, even in the best studied model organisms.

Can efficient algorithms find approximately optimal solutions? The classical theory of NP-completeness didn't address or preclude this possibility.

This talk presents a strategy based on systematic gene perturbation and innovative multiplex detection to derive regulatory networks in mammalian cells.

This talk presents the normalization model of neural computation and elaborates the hypothesis that dysfunctions of normalization may be associated with schizophrenia, amblyopia, epilepsy, and autism spectrum disorders.

The modern ability to carefully measure large-scale social networks has driven new empirical studies and theoretical models of growth, dynamics, influence, and collective behavior in such systems.

Man has grappled with the meaning and utility of randomness for centuries. This talk describes two main aspects of the research on randomness with respect to the theory of computation, demonstrating respectively its power and weakness for making algorithms faster.