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.
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.
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.
Bacteria have developed an astonishing panoply of survival strategies in varying environments. This talk describes some recent experimental and theoretical studies connected with microbial behavior.
This talk presents a strategy based on systematic gene perturbation and innovative multiplex detection to derive regulatory networks in mammalian cells.
Can efficient algorithms find approximately optimal solutions? The classical theory of NP-completeness didn't address or preclude this possibility.
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.
