Stochastic fluidic assembly is an approach to small scale fabrication that serves as an alternative to both top-down pick-and-place assembly and bottom-up selfassembly. It avoids the complications of top-down assembly by relying on stochasticity in the environment for component transportation and local self-assembly forces for component positioning. However, unlike pure self-assembly approaches, stochastic assembly is dynamically programmable and can assemble arbitrarily specified (nonregular, nonrandom) structures. The work presented here advances the state of the art of stochastic fluidic assembly with contributions in four areas. The first area of contribution is the demonstration of stochastic fluidic assembly at the microscale. Previous work in dynamically programmable stochastic assembly (fluidic or otherwise), has been done at the cm or dm scales. The work in Part I of this dissertation describes experiments that demonstrate the assembly of arbitrary structures composed of up to 10 microcomponents, the first steps to adding functionality to the components, and hierarchical approaches for the acceleration of assembly. These advances were achieved by taking an approach that minimizes the complexity of the components required for assembly. The second area of contribution, presented in Part II, is the demonstration of robust 3-D assembly at the cm scale. Previous stochastic assembly approaches have demonstrated either 2-D assembly, or 3-D planar assembly at the dm scale. Again, these contributions were made possible by developing a stochastic assembly that minimizes the required module functionality. Part III of this dissertation presents work relating to the third area of contribution, computationally efficient simulation of stochastic fluidic assembly. The ability to simulate stochastic fluidic assembly is invaluable in system design, assembly algorithm development, and assembly time and error prediction. However, the standard method of simulating fluid-structure interaction-involving Computational Fluid Dynamics (CFD)-is very computationally expensive. In Part III, a custom simulator is presented that makes simplifications where possible, and is tested against CFD results and experiments. The final area of contribution, presented in Part IV of this dissertation, is in the development of assembly strategies for stochastic fluidic assembly. This work presents a set of strategies developed and evaluated in simulation. Additionally, a novel approach is presented for stochastic assembly (fluidic or otherwise) that analyzes a target structure and finds valid assembly sequences on-the-fly.