  Clifford analysis has been the field of active research for several decades
resulting in various methods to solve problems in pure and applied mathematics.
However, the area of stochastic analysis has not been addressed in its full
generality in the Clifford setting, since only a few contributions have been
presented so far. Considering that the tools of stochastic analysis play an
important role in the study of objects, such as positive definite functions,
reproducing kernels and partial differential equations, it is important to
develop tools for the study of these objects in the context of Clifford
analysis. Therefore, in this work-in-progress paper, we present further steps
towards stochastic Clifford analysis by studying random variables, martingales,
Brownian motion, and It\^o formula in the Clifford setting, as well as their
applications in Clifford analysis.
