School of Physics and Astronomy

Machine Learning Seminar: Deep Learning applications for the Large Hadron Collider

At the LHC, physicists need to process large amounts of data in short time. This is typically done with rule-based algorithms designed through domain knowledge, which can reach very large accuracy at the cost of big execution time.

4 June 2019

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The emergence of Deep Learning as a tool to process raw data is opening new possibilities, both in terms of accuracy and latency. Dr Maurizio Pierini (CERN) will give a special Machine Learning Seminar for the School of Physics and Astronomy at QMUL on Monday June 10th 2019.

In his lecture, Dr Pierini will review a set of examples showing how Deep Learning could be used in typical event reconstruction problems at the LHC. Besides offering a cost-effective alternative to classic algorithms, Deep Learning offers the opportunity to explore new methods to probe the existence of physics beyond the Standard Model.

Dr Maurizio Pierini is a CERN research staff and ERC grant holder. In 2009, he has been awarded the European Physical Society's Young Particle Physicist Prize.

Join us in room 410, fourth floor of G.O.Jones Building in Mile End Campus
at 2pm on Monday June 10th, 2019.

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