What kind of dataflow approach are you most interested in? I've been shifting my approach to work within existing systems. One things I've been doing is abusing the intent of web-components and DOM events to create data visualization tools.
Recently my team and I decided to focus down a little more. Have you had success in bridging the gap between esoteric academic papers and industry?
@hmehta, one of my recent favorite videos generally on this topic has been from Curry On conference [1]. It's not strictly data-flow vs control flow, but it does discuss some of the more practical differences in between using monads for structuring data flows vs imperative control.
My take is that monads can be roughly considered as wrapping up imperative control flow along with the data. E.g. wrapping up the if/else choices into a data structure. Currently I actually found studying up on actual category theory easier than all the "easy introduction" things regarding monads. As in, the ideas when presented straightforwardly and technically correct are simpler than we give credence to.
Actually, it feels like I've been "scooped" somewhat. However, at the same time it's awesome to find that many of the core assumptions I've been working in formulating for traditional programming systems are actually already being used by other top engineers/researchers at Google. Thanks for posting about the whos-who of systems research. I'll have to check up on their research more!
I think there is a lot of room left to apply this approach to a lot of existing systems outside of machine learning. We're working on releasing a prototype in the coming months at our new startup (vexpr.com) based on applying these same data-flow principles to normal programming projects. Actually this might give a huge boost in presenting the underlying approach to systems engineering to audiences.