I'm preparing an individual entry, and would be interested in contributing to your team as well.
My contribution would be on the implementation level, I don't know the theoretical side well enough...
What I could contribute is turning those equations into python, and develop the existing code base from it's current upstream beta state. Refactoring to Python 3.5, PEP8, restructuring the Round1 repository to a standard environment as a first step. To be followed by a release of the CI Tested and linted code base in a number of portable formats, Docker, SNAP - a relatively new Ubuntu format, and Virtualbox image files.
If you are serious about "Matter of fact, will pay for it, let me know your conditions." A contribution to help keep me afloat while deticating time to this opens up much greater possibiltlies post refactor, such as providing the package and interactive training to a format the data science guys will love:
Which can be crafted to provide a wonderful combination of code and display, further lowering the entry bar, and adding a partial abstraction from the python syntax for a simplified theory to tested results work flow.
I feel very strongly about the importance of the underlying code is to any serious progress in collaborative machine learning development or study.
Along that sentiment, I got a good chuckle from another Team who referred to the implementation side as... 'just a simple coder. I think their perspective may be altered a bit when they are in fact implementing their theory's.