Dustyn Hofer

My research:

I work on Di-Higgs and Higgs-to-invisible searches with the ATLAS detector.

My expertise is:

Fake-tau background estimation in the HH->bbtautau channel and forward jet modelling in the VBF+MET H->invisible search.

A problem I’m grappling with:

Connecting a series of analysis packages (nTuple generation, parallel processing, ML toolkits) together into one consistent, reproducible analysis pipeline.

I’ve got my eyes on:

Ways to use our physics knowledge to ask the right questions for the right kinds of machine learning architectures to take best advantage of their properties.

I want to know more about:

How different ML architectures are more or less helpful for certain classes of problems.