Is there a symbol for the median of a population? We usually use $\mu$ for the population mean but I couldn't find a symbol the median of a population. Is it ok to use just $m$? For instance, 
$H_o: \ m_{a} = m_{b}$
$H_a: \ m_{a} <> m_{b}$
is it Ok to formulate these hypotheses like that?
 A: I have seen a number of symbols used to denote the population median.
One I have seen quite a few times is $\tilde\mu$, but as whuber suggests in comments, you should define whatever you do use. So if you were to use this suggestion, you could say something like:

$H_0: \ \tilde{\mu}_{a} = \tilde{\mu}_{b}$
  $H_a: \ \tilde{\mu}_{a} \neq \tilde{\mu}_{b}$  
$\text{where }\tilde{\mu}\text{ denotes the population median.}$

It would be okay to use $m$ for that, just as you have it in your question (as long as you define it) -- though keep in mind that conventionally population quantities are denoted by Greek symbols, which is probably why $\tilde\mu$ tends to crop up. 
You'd also need to be clear (somewhere) about the meaning of the subscripts $_a$ and $_b$. (Note also the use of $\neq$ in preference to $<>$.)
A: I am currently in an introduction to statistics class and the textbook says that the median of a statistic can be represented with an "M" (a capital m). And the median of a parameter can be represented with a "θ" (a theta). This is as straightforward of an answer as you are going to get, though I am sure the way you handled the situation would work just fine also.
A: The Greek letter Eta (η) can also be used to represent the population median. Check this from Wikipedia: In Statistics, η2 is the "partial regression coefficient". η is the symbol for the linear predictor of a generalized linear model, and can also be used to denote the median of a population, or thresholding parameter in Sparse Partial Least Squares regression.
A: You can refer to the median as $Q(a,50\%)$, i.e. the 50th percentile, and write the null hypothesis as:
$$H_0: Q(a,50\%)=Q(b,50\%)$$
This notation is longer than the other notations here, but its meaning is probably more obvious. So you probably would want to clarify that $Q$ is the quantile function, but readers might understand the reference to the median automatically.
