A little more about me

Hi there! I'm Henry Truong, a postgraduate researcher in the field of particle physics phenomenology. I'm enrolled in the Data Intensive CDT programme at Durham University where I have been investigating the use of machine learning to accelerate simulated particle collision events, a crucial part of the international Large Hadron collider experiment.

My thesis is focused on applying deep neural networks to emulate expressions depicting scattering probabilities in order to accelerate the simulation process. I have published three papers on this subject. I have shown that speed-ups of factors of 10000 are achievable in ideal settings and over factors of 100 in more realistic production pipelines.

Some other projects I've worked on during my PhD include: an epidemiology model investigating the spread of an infectious disease across individuals in a digital twin of England built upon Office for National Statistics data; and an image post-processing pipeline for enhancing the fidelity of medical x-ray images. You can read more about these on my projects page.

I am interested in applying my modelling, data analysis, and data visualisation skills obtained during my doctoral studies to contribute to a team working on practical challenges.