CERN openlab summer students arrive at CERN


Today, the first group of students participating in the 2019 CERN openlab summer-student programme arrived at CERN. The remaining students will arrive over the next two weeks. In total, 40 students have been selected from 19 countries.

Elisabeth Petit-Bois sélectionnée parmi 1660 étudiants de 19 pays pour intégrer le CERN Openlab 2019 en Suisse

Elisabeth Petit-Bois participera cet été à un programme du CERN, l’Organisation européenne pour la recherche nucléaire, l’un des plus grands et des plus prestigieux laboratoires scientifiques du monde

Going Global

Kennesaw State University’s Elisabeth Petit-Bois has spent most of her undergraduate career researching how humans can integrate artificial intelligence into everyday life. This summer, she will work to perfect her craft as she becomes one of 40 students across the globe and one of only two U.S. students to participate in the CERN openlab Summer Student Programme.

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We are developing a platform that will support a complete data-analysis life cycle, from data discovery through to access, processing, and end-user data analysis. The platform will be easy to use and will offer narrative interfaces.

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We are aiming to create a platform through which life scientists can easily create, run, and visualise three-dimensional biological simulations. Built on top of the latest computing technologies, the BioDynaMo platform will enable users to perform simulations of previously unachievable scale and complexity, making it possible to tackle challenging scientific research questions.

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Our aim is to make control systems used for the LHC more efficient and “smarter”. We are working to enhance the functionality of WinCC OA (a SCADA tool used widely at CERN) and to apply data-analytics techniques to the recorded monitoring data, in order to detect anomalies and systematic issues that may impact upon system operation and maintenance.

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We are developing fast-simulation tools based on machine learning — rather than primarily using classical Monte Carlo — to simulate particle transport in the detectors of the LHC experiments. Such tools could play a significant role in helping the research community to cope with the LHC’s increasing computing demands.

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We are investigating the performance of distributed learning and low-latency inference of generative adversarial networks (GANs) for simulating particle collision events. The performance of a deep neural network is being evaluated on a cluster consisting of IBM Power CPUs (with GPUs) installed at CERN.

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