Conveners
Plenary: Keynotes 1
- Steven Schramm (Universite de Geneve (CH))
Plenary: Plenaries 1
- Steven Schramm (Universite de Geneve (CH))
Plenary: Public lecture
- Guy Paic (Universidad Nacional Autonoma (MX))
Plenary: Keynotes 2
- RAFAEL MAYO (CIEMAT)
Plenary: Plenaries 2
- Alan Paic (OECD)
Plenary: Plenaries 3
- Boris Escalante-Ramírez (UNAM)
Plenary: Keynotes 3
- Gergely Gabor Barnafoldi (Wigner RCP Hungarian Academy of Sciences (HU))
Plenary: Plenaries 4
- Gergely Gabor Barnafoldi (Wigner RCP Hungarian Academy of Sciences (HU))
Plenary: Keynotes 4
- Federico Carminati (CERN)
Plenary: Plenaries 5
- Federico Carminati (CERN)
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Dan Faggella (CEO/founder of Emerj Artificial Intelligence Research)21/10/2019, 09:30Oral
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Michael Aaron Kagan (SLAC National Accelerator Laboratory (US))21/10/2019, 10:15Oral
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Nathalie Rauschmayr (Amazon Web Services AI)21/10/2019, 11:30Oral
Deep learning is driving rapid progress in fields such as computer vision, natural language processing, and speech recognition. It has become one of the most disruptive technologies and nowadays many products feature artificial intelligence. However, creating production-ready deep learning models involves many challenges: starting from generating good high-quality training datasets to...
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Mr Saul Alonso Monsalve (CERN)21/10/2019, 12:00Oral
This talk will cover the current state of machine learning (ML) in neutrino experiments. In experiments like the Deep Underground Neutrino Experiment (DUNE), NuMI Off-axis νe Appearance (NOvA), the Micro Booster Neutrino Experiment (MicroBooNE), and Argon Neutrino Teststand (ArgoNeuT), deep learning (DL) approaches based around convolutional neural networks have been developed to provide...
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Pedro Mario Cruz e Silva (NVidia)21/10/2019, 12:30Oral
In this talk I will present technical details about the ACM Gordon Bell Prize winner project at Supercomputing 2018. In this work the joint team from NERSC and NVIDIA succeeded in scaling a Deep Learning training across 27.000+ GPUs in Summit (world’s largest HPC system) and obtained a high fraction of peak performance. This research showed that realistic scientific applications could leverage...
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Cédric Bourrasset (Atos)21/10/2019, 18:00Oral
This lecture will cover the AI research challenges currently addressed by Atos research teams over different domains like Cyber Security, Predictive Maintenance of IT and Privacy by Design needs for Video Intelligence solution in the context current European regulations.
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Ulises Cortes (Barcelona Supercomputing Center)22/10/2019, 09:00Oral
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Mirjana Stankovic (Vice President, Emerging Tech and Intellectual Property, Tambourine Innovation Ventures)22/10/2019, 09:45Oral
“Can regulators keep up with fintech?” “Your Apps Know Where You Were Last Night, and They’re Not Keeping It Secret.” “Regulators scramble to stay ahead of self-driving cars.” “Digital health dilemma: Regulators struggle to keep pace with health care technology innovation.” Headlines like these capture a central challenge to today’s regulators.
Existing regulatory structures are often slow...
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Tobias Golling (Universite de Geneve (CH))22/10/2019, 11:00Oral
High-energy particle physic experiments rely heavily on billions of CPU hours per year for data processing purposes. The production of synthetic data through Geant4-based Monte Carlo simulation describing particle shower developments in the calorimeter is the single most compute-intensive tasks. Fast simulation techniques are already today indispensable. With the upcoming high-luminosity...
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Benjamin Sanchez-Lengeling (Google Brain)22/10/2019, 11:30Oral
Many of the challenges of the 21st century, from personalized healthcare to energy production and storage, share a common theme: materials are part of the solution. Groundbreaking advances are likely to come from unexplored regions of chemical space. A central challenge is, how do we design molecules and materials according to a desired functionality?
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In this talk I showcase how we can apply... -
George Djorgovski (California Institute of Technology)22/10/2019, 12:00Oral
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Panagiotis Barkoutsos (IBM Zurich)22/10/2019, 12:30Oral
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Jonas Glombitza (RWTH Aachen University)22/10/2019, 14:30Oral
Ultra-high energy cosmic rays (UHECRs) are the most energetic particles found in nature and originate from extragalactic sources. These particles induce extensive air showers when propagating within the Earth’s atmosphere. Cosmic-ray bservatories like the Pierre Auger Observatory measure such air showers using large arrays of surface-detector stations and luorescence telescopes. The...
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Cédric Bourrasset (Atos)22/10/2019, 15:00Oral
Science and numerical applications are evolving towards integrating Machine Learning based algorithms. This talk will review future impacts on upcoming HPC architectures.
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Celia Escamilla-Rivera (ICN-UNAM)22/10/2019, 15:30Oral
In this talk I will describe ongoing efforts to shed light on still-unanswered questions in fundamental physics using cosmological observations. I will explain how we can use measurements of the Supernovae data, Baryon Acoustic Oscillations, Cosmic Microwave Background and the large-scale structure of the universe to reconstruct the detailed physics of the dark universe. Also I will address...
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Luis Pineda (Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, UNAM)22/10/2019, 16:00Oral
The Turing Machine is the paradigmatic case of computing machines, but there are others, such as Artificial Neural Networks, Table Computing, Relational-Indeterminate Computing and diverse forms of analogical computing, each of which based on a particular underlying intuition of the phenomenon of computing. This variety can be captured in terms of system levels, re-interpreting and...
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Pablo Meyer (IBM, Thomas J. Watson Research Center)24/10/2019, 09:00Oral
The generation of large-scale biomedical data is creating unprecedented opportunities for applications of AI solutions. Typically, the data producers develop initial predictions using AI, but it is very likely that the higher performing AI methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies in...
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Željko Ivezić (University of Washington)24/10/2019, 09:45Oral
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Jan Kieseler (CERN)24/10/2019, 11:00Oral
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Isidoro Gitler (CINVESTAV)24/10/2019, 11:30Oral
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Juan Cerrolaza (Accenture Iberia - Artificial Intelligence Group)24/10/2019, 12:00Oral
With the advent of deep learning-based techniques, new approaches and novel architectures are proposed every day, improving the state of the art in almost every technical discipline, including medical imaging. Autoencoders are one of the most popular unsupervised deep learning techniques. Thanks to their unsupervised nature, autoencoders, and its several variants, such as variational...
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Christian Eduardo Hernandez Garcia (Huawei), Su Quing (Huawei)24/10/2019, 12:30
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Jesús Savage (UNAM)25/10/2019, 09:00Oral
In this talk it is presented the semantic-reasoning module of VIRBOT, our proposed architecture for service robots.
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We show that by combining symbolic AI with digital-signal processing techniques this module achieves competitive performance.
Our system translates a voice command into an unambiguous representation that helps an inference engine, built around an expert system, to perform... -
Scott Hamilton (Atos)25/10/2019, 09:45
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Frederik Van Der Veken (University of Malta (MT))25/10/2019, 11:00Oral
With the advent of machine learning a few decades ago, Science and Engineering have had new powerful tools at their disposal. Particularly in the domain of particle physics, machine learning techniques have become an essential part in the analysis of data from particle collisions. Accelerator physics, however, only recently discovered the possibilities of using these tools to improve its...
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Long-Gang Pang (CCNU & LBL)Oral
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Vanessa Hernandez (IBM)
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Kyle Stuart Cranmer (New York University (US))Oral
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Scott Hamilton (Atos/Bull)