Hello and welcome to my blog / personal website! My name is Giles, I’m a researcher of particle physics and machine learning, and obtained my PhD in physics from IST, Lisbon in 2021.

Currently I am working as a researcher at INFN-Padova. My day to day work takes place mostly within the context of the CMS experiment at CERN, and my particular research interests revolve around developing, adapting, and applying machine learning methods to the various domain-specific problems we face in analysing high-energy physics (HEP) data.

My current research now includes:

  • Searching for di-Higgs production in the bb𝜏𝜏 decay channel with CMS (paper);
  • Developing regression models for muon energy measurement in calorimeters (paper);
  • Systematics-aware training methods for models (see posts on INFERNO, and pytorch_inferno package);
  • End-to-end optimisation of detector design as part of the newly-formed MODE collaboration. In particular, I focus on detector optimisation for muon-tomography as a core developer of the TomOpt package; checkout section 4.3 in our recent whitepaper, or my blog post)

Additionally I am the core developer for LUMIN, a PyTorch wrapper library allowing researchers to easily apply the latest training methods and architecture for deep neural networks to their analysis of HEP data. Please see this paper for an example.

Please see my publications and presentations pages for further details. I can be found on GitHub and Twitter, and am contactable by email at giles.chatham.strong (@) cern.ch.