Back to Innovate with CERN.
CERN experts shared their expertise on machine learning with Sanofi Pasteur
A four-day training course tailored to address topics specifically of interest to Sanofi-Pasteur, with the aim of improving vaccine production. The course was built around ROOT, the data analysis framework used to analyse HEP data, and the Toolkit for Multivariate Data Analysis (TMVA), a library of associated machine learning algorithms. The main objective of the course was to apply novel machine learning techniques to various vaccine production challenges that had proven hard to solve using conventional methods.
Software solutions for autonomous driving
Zenseact (formerly Zenuity), a company developing software solutions for automotive safety and autonomous driving (AD), has become the first to team up with CERN in the fields of fast machine learning.
A fundamental challenge in the development of AD cars is the fast interpretation of the huge quantities of data generated in normal driving conditions. CERN has approached this challenge in the context of physics data acquisition, by using Field-Programmable Gate Arrays (FPGAs) hardware that can execute complex decision-taking algorithms in microseconds. The collaboration between Zenuity and CERN aims to leverage this knowledge using FPGAs for fast machine learning applications that allow AD cars to reach fast decisions and make predictions more quickly.
(Image Credit: Zenseact)
Bundesdruckerei works with CERN on future identity concepts and cryptography
Bundesdruckerei GmbH has started to work with CERN, the European Laboratory for Particle Physics on research exploring possible links between quantum physics, identities and trust, with a focus on how findings from quantum physics can be transferred to IT systems designed for security.
New approaches and concepts are to be developed in identity management and cryptography based on quantum mechanical phenomena.The aim is to actively prepare for the age of quantum computers and thus help to protect companies, organizations and citizens. The goal is to find ways in which quantum mechanical functions can be used to develop a secure digital identity that is also easy to use.
(Image credit: Jacqueline Macou from Pixabay)
Explore more stories of successful collaborations at kt.cern