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Data Science @ LHC 2015 Workshop

9–13 Nov 2015
CERN
Europe/Zurich timezone
There is a live webcast for this event.

Session

Wednesday Morning Session

11 Nov 2015, 09:00
222/R-001 (CERN)

222/R-001

CERN

200
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Conveners

Wednesday Morning Session

  • Maria Spiropulu (California Institute of Technology (US))

Presentation materials

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  1. 10. Data science in ALICE
    Michele Floris (CERN)
    11/11/2015, 09:00
    ALICE is the LHC experiment dedicated to the study of Heavy Ion collisions. In particular, the detector features low momentum tracking and vertexing, and comprehensive particle identification capabilities. In a single central heavy ion collision at the LHC, thousands of particles per unit rapidity are produced, making the data volume, track reconstruction and search of rare signals...
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  2. 20. Deep Learning and its Applications in the Natural Sciences
    Pierre Baldi (UCI)
    11/11/2015, 09:45
    Starting from a brief historical perspective on scientific discovery, this talk will review some of the theory and open problems of deep learning and describe how to design efficient feedforward and recursive deep learning architectures for applications in the natural sciences. In particular, the focus will be on multiple particle problems at different scales: in biology (e.g. prediction of...
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  3. 21. A ground-up construction of deep learning
    Luke Percival De Oliveira (SLAC National Accelerator Laboratory (US))
    11/11/2015, 10:45
    I propose to give a ground up construction of deep learning as it is in it's modern state. Starting from it's beginnings in the 90's, I plan on showing the relevant (for physics) differences in optimization, construction, activation functions, initialization, and other tricks that have been accrued over the last 20 years. In addition, I plan on showing why deeper, wider basic feedforward...
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  4. 33. Neuromorphic silicon chips
    Giacomo Indiveri (INI Zurich), Sim Bamford (INI Labs)
    11/11/2015, 11:30
    Neuromorphic silicon chips have been developed over the last 30 years, inspired by the design of biological nervous systems and offering an alternative paradigm for computation, with real-time massively parallel operation and potentially large power savings with respect to conventional computing architectures. I will present the general principles with a brief investigation of the design...
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  5. 17. Artificial Intelligence and the Future of Science
    Demis Hassabis (Google DeepMind)
    11/11/2015, 12:15
    Dr. Demis Hassabis is the Co-Founder and CEO of DeepMind, the world’s leading General Artificial Intelligence (AI) company, which was acquired by Google in 2014 in their largest ever European acquisition. Demis will draw on his eclectic experiences as an AI researcher, neuroscientist and videogames designer to discuss what is happening at the cutting edge of AI research, its future impact...
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