Complexity Postdoctoral Fellow
Omidyar Fellow, Baird Scholar
Every biological organism is computational, gathering information about its immediate environment, recalling traces of past environments, processing noisy information, and making decisions about its next course of action. Albert is fascinated by the diversity of strategies that organisms have evolved to reliably make good decisions while constrained by limited resources and unreliable tools.
The unique abilities available to collective systems are of particular interest to Albert, which, broadly defined, can include animal groups such as bird flocks, plant root systems, fungal networks, and human social networks. Such systems can potentially detect features of their environment invisible to spatially-constrained organisms, perform complex computations in parallel, and enhance their decision-making ability through consensus forming mechanisms. To understand the capabilities, and constraints, that collective systems face, Albert couples theoretical models and computational simulations with experiments on a wide range of organisms, including slime molds, army ants, fish, homing pigeons, and humans. Using this broad approach, he hopes to uncover both general principles common to collective systems, as well as specific solutions discovered by particular taxa.
Prior to joining SFI, Albert Kao was a James S. McDonnell Foundation postdoctoral fellow. He received his Ph.D. from Princeton University, supervised by Iain Couzin, and an A.B. in physics from Harvard College.