[kumarski]

What are your thoughts on Bayes Impact?

In the world of data science, 99% of the problem is defining the problem. There's huge armies of data scientists ready to tackle well defined problems.

How do you hope to do a really good job of defining the problem in a meaningful insightful unconventional way? What are your tactics to do so?

The problem with ResearchKit is that it's hunting for Hawthorne-effect-less data even though it's rife with it.

The issue with a lot of the Validic-style MEMS data is that there's no indication that walking/running/steps counted/ or other key MEMS recorded activities account for a gene regulation pathway?

I haven't seen any papers on meaningful environmental factors being accrued from MEMS collected data.

Neurogenerative disorders can't be modeled easily from early onset symptoms. While from a data perspective it's exciting, many bioinformaticists might disagree. I might be wrong though.

Using a different example could be a good move.

Godspeed.