Adjunct Professor, Biochemistry and Molecular Biophysics, Columbia University
Analyzing the collective behavior and social interactions of hundreds of millions of people with powerful computational techniques offers a radically new perspective on fundamental questions in the social sciences.
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.
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.