Noah Clarke Hall

I chose the CDT because of the unique emphasis on data science and ML and broad range of research themes.

Project title: Deep learning the shape of the Higgs potential with ATLAS at the LHC 

Research Group: High Energy Physics

Supervisor(s):  Prof Nikos Konstantinidis

Introduction: 

I am a 2nd year PhD student in the CDT working at the intersection of Machine-Learning (ML) and High Energy Physics. I chose the CDT because of the unique emphasis on data science and ML and broad range of research themes. I believe it is important to collaborate with students and academics from different research backgrounds and the CDT is a great place to do that.

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Project description:  

I work on the ATLAS non-resonant Higgs self-coupling measurement, using cutting edge ML techniques to discriminate and model backgrounds in this important and exciting analysis. The measurement has important implications for how SM particles gain their mass and even the stability of the vacuum.

I also work on algorithm R&D for the ATLAS hardware trigger, developing innovative ultra-fast ML algorithms for nanosecond inference on FPGAs.

First year group project: Tracing anti-microbial resistance using wastewater gene concentrations in Wales

Placement: 

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