Natural search algorithms

I am becoming increasingly interested in understanding how biological systems interact with information. Search behavior provides a natural model for studying this phenomenon. Searching organisms execute search algorithms, which involve collecting data, extracting information from those data, and responding in a way that allows them to locate a target. I use theory and experiments to study natural search algorithms and explore their implications for ecological and evolutionary dynamics. These studies draw on many areas, including neuroscience, sensory biology, biomechanics, behavior, ecology and evolution. Moreover, there are many parallels between natural search algorithms and applications such as numerical optimization, control, and engineered search.

Some specific topics are:
Evolution of cooperative search (theoretical work with Levin lab)
Sensory information and encounter rates (theoretical work with Scott McKinley)
Sparse-signal search (theoretical work with Scott McKinley)
Visual information networks and evasion by reef fish (experimental work with Mike Gil)

Papers in this area:

Hein, A M, F Carrara, D. Brumley, R Stocker, and S A Levin. Natural search algorithms as a bridge between organisms, evolution, and ecology. PNAS.

HeinA M D. Brumley, F Carrara, R Stocker, and S A Levin. 2016. Physical limits on bacterial navigation in dynamic environments. J. Royal Society Interface.

Park, I, A M Hein, Y V Bobkov, M A Reidenbach, B Ache, and J C Principe. 2016. Neurally encoding time in olfactory search. PLoS Comp. Biol.

Hein, A M, S B Rosenthal, G I Hagstrom, A Berdahl, C J Torney, and I D Couzin. 2015. The evolution of distributed sensing and collective computation in animal populations. eLife 

Hein, A M and S A McKinley. 2013. Sensory information and encounter rates of interacting species. PLoS Comp. Biol.

Hein, A M and S A McKinley. 2012. Sensing and decision-making in random search. PNAS.