Application of Machine Learning Methods in the Data Analysis at the Large Hadron Collider (LHC)

With the increasing complexity of the research in experimental particle physics, looking for new physics signatures in progressively larger and more complex data sets that are being analyzed at the LHC experiments, new approaches to data analysis, from reconstruction to simulation, need to be investigated. The main objective of this project is to develop and test state-of-the-art scientific tools for HEP data simulation, reconstruction and analysis, using software technologies based on Machine Learning in general and Deep Learning in particular. These tools will be executing on the newest (accelerator-enabled) hardware solutions in the HPC super-computing clusters, in order to address the challenges of speed and accuracy, crucial for the existing and next generation of High Energy Physics (HEP) Collider experiments.