Deep Work, Better Engineering, and Machine Learning Research

I’m a big fan of Cal Newport’s work and, recently, have laid out a three-month plan to immerse myself more deeply in becoming a better engineer through functional programming, and to experimenting in the pursuit of machine learning research as my specialized expertise.

This really isn’t anything new. This three-month plan was a comprehensive breakdown and milestone planning of CSSR, which I introduced in a previous post. Primarily, I was able to scope out some pragmatic deadlines and reach some good conclusions: including that publication comes before everything else and that I will write CSSR in scala first to offset the added cost of learning the extremely pure functional programming paradigm, then keep an experimental feature branch running of the same work in Haskell - even though it might blow the scope of this project out of proportion.

Quite frankly, this makes me nervous: I will have to account for the unknowns of work, make ICML and KDD call-for-paper deadlines, and do it all in two languages (which may be narrowed in scope down to one language). I’m intending to make a number of social and personal sacrifices to make these deadlines, all self-imposed.

Today has been the big scrub of Shallow Work – tomorrow “Deep Work” begins in the morning, and I will post updates of my progress every week in the form of blog posts on CSSR, Scala, or Haskell.