I really enjoyed this writeup. Having just finished a year in the field on the engineering side, and reading many papers without an academic background, it’s funny to recognize who are the “math” types and who are the “biology” types at work. Puts things in perspective.

I’ve been pushing for more ensembles and multi-label classifiers because I want to orchestrate the pieces with logic to fill gaps until stats methods outperform (given enough new data in nice annotation-friendly structures).

The “math” types seem to feel like there’s a neural net solution to every problem or that we can expand the multi class model to cover more domains despite being mostly saturated with high accuracy. Sounds awesome but I’m impatient!

The “biology” folks seem to be most attracted to neatness or parsimony. We had some great bikeshedding sessions around adjacency list vs materialized path (ltree) in postgres for label hierarchies. Abstractions can be useful too!

Any tips on being a better experimentalist and pushing academic colleagues towards better solutions in the field?