Short presentation

Troels is an experimental particle physicist, working mainly on the ATLAS experiment at CERN but also on the Icecube experiment on the South Pole.

His main research interests are in the areas of Particle Physics (LHC, Higgs decays, searches), Machine Learning (geometric learning, combining methods, application to science+ cases), Statistics & Data Analayis (data reduction, fitting, hypothesis testing), and Calibration (pushing detector performance and understading to the limit).

He teaches statistics and Machine Learning and supervise a significant number of master students. Troels won the Science Faculty Dissemination Prize in 2015 and the Teaching Award of the faculty in 2021.

CV

I studied for my Ph.D. in Paris working on the BaBar experiment at Stanford 2001-2004. Since, I have have been working mainly on the ATLAS experiment, where I have contributed to electron identification in particular with the Transition Radiation Tracker (TRT), worked on both the W mass measurement, searchers for stable massive particles, the Higgs discovery, and the search for additional Higgs particles and decays. I have been know for my application of Machine Learning (ML) to the ATLAS data analysis.

Lately, I have also been involved in the IceCube experiment on using Graph Neural Networks for event classification and reconstruction.

Generally, I am a ML enthusiast and avid user, having applied ML to many cases in particle physics and beyond. I teach two large and relatively acclaimed courses on applied statistics and ML, respectively.

In my (sparse) spare time, I love traveling to far off exotic places, sailing, and playing football, squash, tennis, and strategy games.