Reliable ML systems
outside the lab.

I build machine learning systems that are supposed to work in the real world — then I find out where they don't.

I'm Yuhe Lu 陆禹翮, studying Mathematics and Computing Science at the University of Glasgow, graduating in 2027. Most of what's on this page comes out of one habit: build something, then go looking for the conditions under which it stops working.

My clearest example so far is a project segmenting crop residue in field images. The model did well in testing, but accuracy fell on locations it hadn't seen before, and it did worst on the highest-coverage images — a condition the training data barely represented. That failure taught me more about what "reliable" actually requires than any of the cases where the model worked fine.

That's the question I want to keep asking: whether it keeps performing once the conditions around it change. One project isn't enough to answer that yet, but it's the direction I want the rest of this to go.